This is a general presentation of the 3W dataset, to the best of its authors' knowledge, the first realistic and public dataset with rare undesirable real events in oil wells that can be readily used as a benchmark dataset for development of machine learning techniques related to inherent difficulties of actual data.
For more information about the theory behind this dataset, refer to the paper A Realistic and Public Dataset with Rare Undesirable Real Events in Oil Wells published in the Journal of Petroleum Science and Engineering (link here).
This Jupyter Notebook presents the 3W dataset in a general way. For this, some tables, graphs, and statistics are presented.
import sys
import os
import pathlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import re
import tensorflow as tf
#NUM_PARALLEL_EXEC_UNITS = 6
#config = tf.compat.v1.ConfigProto(intra_op_parallelism_threads = NUM_PARALLEL_EXEC_UNITS,
# inter_op_parallelism_threads = 1,
# allow_soft_placement = True,
# device_count = {'CPU': NUM_PARALLEL_EXEC_UNITS })
#session = tf.compat.v1.Session(config=config)
#tf.compat.v1.keras.backend.set_session(session)
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras import regularizers
import tensorflow_addons as tfa
from util_3WT import d3w, CustomDataGen
import pickle
import bisect
import sklearn
import sklearn.model_selection
from sklearn.metrics import classification_report, ConfusionMatrixDisplay, multilabel_confusion_matrix, confusion_matrix
from sklearn.utils.class_weight import compute_class_weight
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 4132666557891900372
xla_global_id: -1
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 1422540800
locality {
bus_id: 1
links {
}
}
incarnation: 13611297491031944386
physical_device_desc: "device: 0, name: NVIDIA GeForce GTX 950, pci bus id: 0000:01:00.0, compute capability: 5.2"
xla_global_id: 416903419
]
Below, all 3W dataset's instances are loaded and the first one of each knowledge source (real, simulated and hand-drawn) is partially displayed.
if pathlib.Path('dset.pkl').exists():
with open('dset.pkl', 'rb') as f:
dset = pickle.load(f)
else:
dset = d3w('../dataset')
with open('dset.pkl', 'wb') as f:
pickle.dump(dset, f)
flist0 = ['P-PDG', 'P-TPT', 'T-TPT', 'P-MON-CKP', 'T-JUS-CKP', 'P-JUS-CKGL', 'T-JUS-CKGL', 'QGL']
categories=[[0,1,2,3,4,5,6,7,8,101,102,103,104,105,106,107,108]]
train_df, test_df, val_df = dset.split(real=True, simul=True, drawn=True, test_size=0.1, val_size=0.2, sample_n=300)
Instances Train: 213 Test: 30 Validation: 54
Each instance is stored in a CSV file and loaded into a pandas DataFrame. Each observation is stored in a line in the CSV file and loaded as a line in the pandas DataFrame. The first line of each CSV file contains a header with column identifiers. Each column of CSV files stores the following type of information:
Other information are also loaded into each pandas Dataframe:
More information about these variables can be obtained from the following publicly available documents:
The following table shows the amount of instances that compose the 3W dataset, by knowledge source (real, simulated and hand-drawn instances) and by instance label.
class kmodel():
def __init__(self, name, class_model, features, label, categories, batch_size, seq_length,
train_df, val_df, test_df, tmp_path, reset_ts=False, verbose=1, class_bal=False):
self.features = features
self.n_features = len(features)
self.label = label
self.categories = categories
self.n_classes = len(*categories)
self.seq_length = seq_length
self.batch_size = batch_size
self.tmp_path = pathlib.Path(tmp_path)
self.verbose = verbose
self.class_bal = class_bal
self.history = None
self.class_rep_val = None
self.class_rep_test = None
self.class_rep_full = None
self.conf_mat_val = None
self.conf_mat_test = None
self.conf_mat_full = None
self.train = CustomDataGen(train_df, self.features, self.label, categories,
self.batch_size, self.seq_length, self.tmp_path)
self.val = CustomDataGen(val_df, self.features, self.label, categories,
self.batch_size, self.seq_length, self.tmp_path)
self.test = CustomDataGen(test_df, self.features, self.label, categories,
self.batch_size, self.seq_length, self.tmp_path)
self.full = CustomDataGen(pd.concat([train_df, val_df, test_df], ignore_index=True),
self.features, self.label, categories,
self.batch_size, self.seq_length, self.tmp_path)
if reset_ts:
self.full.reset_ts()
self.model = class_model()
self.model.build(input_shape=(None, self.seq_length, 2*self.n_features))
input = tf.keras.Input(shape=(self.seq_length, 2*self.n_features), name='input',
tensor=tf.zeros(shape=(self.batch_size, self.seq_length, 2*self.n_features)))
y = self.model.call(input, training=True)
#for layer in self.model.layers:
# print(self.model.name, layer.name, layer.input_shape, '->', layer.output_shape)
#print('\n')
if self.verbose > 0:
print(self.model.summary())
return
def compile_and_fit(self, max_epochs=20, patience=3, lr=0.001):
if self.class_bal:
y_real = self.train.get_y()
d = dict(zip(np.unique(y_real), compute_class_weight('balanced', classes=np.unique(y_real), y=y_real)))
w = dict()
for i, j in enumerate(categories[0]):
if j in d.keys():
w[i] = d[j]
else:
w[i] = 1.0
else:
w = None
early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_f1_score',
patience=patience,
mode='max')
checkpoint_filepath = self.tmp_path.joinpath('checkpoint')
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
filepath=checkpoint_filepath,
save_weights_only=True,
monitor='val_f1_score',
mode='max',
save_best_only=True)
f1_score = tfa.metrics.F1Score(num_classes=self.n_classes, average='macro', threshold=0.5)
self.model.compile(loss=tf.keras.losses.CategoricalCrossentropy(),
optimizer=tf.keras.optimizers.Adam(learning_rate=lr),
metrics=[tf.keras.metrics.CategoricalAccuracy(),
tf.keras.metrics.Precision(),
tf.keras.metrics.Recall(),
f1_score])
self.history = self.model.fit(self.train, epochs=max_epochs,
validation_data=self.val, class_weight=w,
callbacks=[early_stopping, model_checkpoint_callback])
self.model.load_weights(checkpoint_filepath)
self.class_rep_val, self.conf_mat_val = self.class_rep(self.val)
self.class_rep_test, self.conf_mat_test = self.class_rep(self.test)
self.class_rep_full, self.conf_mat_full = self.class_rep(self.full)
if self.verbose > 0:
self.plot_fithist('f1_score')
self.print_class_rep(self.class_rep_val, 'Val Classification Report')
self.plot_confusion_matrix(self.conf_mat_val, title='Confusion Matrix Val')
self.print_class_rep(self.class_rep_test, 'Test Classification Report')
self.plot_confusion_matrix(self.conf_mat_test, title='Confusion Matrix Test')
self.print_class_rep(self.class_rep_full, 'All Data Classification Report')
self.plot_confusion_matrix(self.conf_mat_full, title='Confusion Matrix Full')
return
# Plot training loss & metric
def plot_fithist(self, met):
N = len(self.history.history['loss'])
plt.style.use("ggplot")
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10,5))
fig.suptitle('Training Loss & '+met)
ax1.plot(np.arange(0, N), self.history.history[met], label="train")
ax1.plot(np.arange(0, N), self.history.history["val_"+met], label="val")
ax1.set_title(met)
ax1.set_xlabel("Epoch #")
ax1.xaxis.get_major_locator().set_params(integer=True)
ax1.set_ylabel(met)
ax1.legend(loc="lower right")
ax2.plot(np.arange(0, N), self.history.history["loss"], label="train")
ax2.plot(np.arange(0, N), self.history.history["val_loss"], label="val")
ax2.set_title("Loss")
ax2.set_xlabel("Epoch #")
ax2.xaxis.get_major_locator().set_params(integer=True)
ax2.set_ylabel("Loss")
ax2.legend(loc="upper right")
plt.show()
return
def save(self):
filename = self.model.name + '_' +\
'batch_size_' + str(self.batch_size) + '-' +\
'seq_length_' + str(self.seq_length) + '-' +\
str(np.datetime64('now')) + '.h5'
filename = filename.replace(':', '_')
self.model.save_weights(filename)
print('model weights saved in ' + filename)
return
def class_rep(self, dset):
pred = self.model.predict(dset)
y_pred = [self.categories[0][i] for i in np.argmax(pred, axis=1)]
y_real = dset.get_y()
return classification_report(y_real, y_pred, zero_division=0, output_dict=True),\
confusion_matrix(y_real, y_pred, labels=self.categories[0])
def print_class_rep(self, data_dict, title):
# extracted from: https://github.com/scikit-learn/scikit-learn/blob/0fb307bf3/sklearn/metrics/_classification.py#L1825
"""Build a text report showing the main classification metrics.
Read more in the :ref:`User Guide <classification_report>`.
Parameters
----------
report : string
Text summary of the precision, recall, F1 score for each class.
Dictionary returned if output_dict is True. Dictionary has the
following structure::
{'label 1': {'precision':0.5,
'recall':1.0,
'f1-score':0.67,
'support':1},
'label 2': { ... },
...
}
The reported averages include macro average (averaging the unweighted
mean per label), weighted average (averaging the support-weighted mean
per label), and sample average (only for multilabel classification).
Micro average (averaging the total true positives, false negatives and
false positives) is only shown for multi-label or multi-class
with a subset of classes, because it corresponds to accuracy otherwise.
See also :func:`precision_recall_fscore_support` for more details
on averages.
Note that in binary classification, recall of the positive class
is also known as "sensitivity"; recall of the negative class is
"specificity".
"""
non_label_keys = ["accuracy", "macro avg", "weighted avg"]
y_type = "binary"
digits = 2
target_names = [
"%s" % key for key in data_dict.keys() if key not in non_label_keys
]
# labelled micro average
micro_is_accuracy = (y_type == "multiclass" or y_type == "binary")
headers = ["precision", "recall", "f1-score", "support"]
p = [data_dict[l][headers[0]] for l in target_names]
r = [data_dict[l][headers[1]] for l in target_names]
f1 = [data_dict[l][headers[2]] for l in target_names]
s = [data_dict[l][headers[3]] for l in target_names]
rows = zip(target_names, p, r, f1, s)
if y_type.startswith("multilabel"):
average_options = ("micro", "macro", "weighted", "samples")
else:
average_options = ("micro", "macro", "weighted")
longest_last_line_heading = "weighted avg"
name_width = max(len(cn) for cn in target_names)
width = max(name_width, len(longest_last_line_heading), digits)
head_fmt = "{:>{width}s} " + " {:>9}" * len(headers)
report = head_fmt.format("", *headers, width=width)
report += "\n\n"
row_fmt = "{:>{width}s} " + " {:>9.{digits}f}" * 3 + " {:>9}\n"
for row in rows:
report += row_fmt.format(*row, width=width, digits=digits)
report += "\n"
# compute all applicable averages
for average in average_options:
if average.startswith("micro") and micro_is_accuracy:
line_heading = "accuracy"
else:
line_heading = average + " avg"
if line_heading == "accuracy":
avg = [data_dict[line_heading], sum(s)]
row_fmt_accuracy = "{:>{width}s} " + \
" {:>9.{digits}}" * 2 + " {:>9.{digits}f}" + \
" {:>9}\n"
report += row_fmt_accuracy.format(line_heading, "", "",
*avg, width=width,
digits=digits)
else:
avg = list(data_dict[line_heading].values())
report += row_fmt.format(line_heading, *avg,
width=width, digits=digits)
print(title)
print(report)
return
def plot_confusion_matrix(self, cm, title='Confusion Matrix'):
con = np.zeros((self.n_classes, self.n_classes))
for x in range(self.n_classes):
for y in range(self.n_classes):
if np.sum(cm[x,:]) > 0:
con[x,y] = cm[x,y]/np.sum(cm[x,:])
plt.figure(figsize=(10,6))
sns.set(font_scale=0.75) # for label size
ax = sns.heatmap(con, annot=True,fmt='.3f', cmap='Blues',xticklabels= self.categories[0] ,
yticklabels= self.categories[0], cbar=False)
plt.tight_layout()
ax.set_xlabel('Predicted', fontsize=16)
ax.set_ylabel('Real', fontsize=16)
ax.set_title(title, fontsize=18)
plt.show()
class kmodel_from_file(kmodel):
def __init__(self, model, input_shape, filename):
self.model = model()
self.model.build(input_shape=(None,)+input_shape)
self.model.load_weights(filename)
self.model.summary()
def class_rep(self, dset):
self.categories = dset.categories
self.n_classes = len(*self.categories)
cr, cm = super().class_rep(dset)
super().print_class_rep(cr, 'Classification Report')
super().plot_confusion_matrix(cm, title='Confusion Matrix')
return
class param_fit():
def __init__(self, name, class_model, features, label, categories, batch_sizes, seq_lengths, learning_rates,
train_df, test_df, tmp_path):
self.name = name
self.class_model = class_model
self.features = features
self.label = label
self.categories = categories
self.batch_sizes = batch_sizes
self.seq_lengths = seq_lengths
self.learning_rates = learning_rates
self.train_df = train_df
self.test_df = test_df
self.tmp_path = tmp_path
self.report = dict()
return
def fit(self, n_splits=5, max_epochs=50, patience=3):
kf = sklearn.model_selection.StratifiedKFold(n_splits=n_splits)
self.report['batch_size'] = []
self.report['seq_length'] = []
self.report['learning_rate'] = []
self.report['cv_n'] = []
self.report['class_rep_val'] = []
self.report['class_rep_test'] = []
for batch_size in self.batch_sizes:
breset = True
for seq_length in self.seq_lengths:
breset = True
for learning_rate in self.learning_rates:
for i, (train_index, val_index) in enumerate(kf.split(self.train_df, self.train_df['label'])):
print('\n', i, batch_size, seq_length, learning_rate,
len(train_index), len(val_index), '\n')
model = kmodel(self.name, self.class_model, self.features, self.label, self.categories,
batch_size, seq_length,
self.train_df.iloc[train_index].reset_index(drop=True),
self.train_df.iloc[val_index].reset_index(drop=True),
self.test_df,
'D:/datatmp', reset_ts=breset, verbose=0)
model.compile_and_fit(max_epochs=max_epochs, patience=patience, lr=learning_rate)
breset = False
self.report['batch_size'].append(batch_size)
self.report['seq_length'].append(seq_length)
self.report['learning_rate'].append(learning_rate)
self.report['cv_n'].append(i)
self.report['class_rep_val'].append(model.class_rep_val)
self.report['class_rep_test'].append(model.class_rep_test)
rep_df = pd.DataFrame(self.report)
rep_df['val_accuracy'] = rep_df.class_rep_val.apply(pd.Series)['accuracy']
rep_df['val_precision'] = rep_df.class_rep_val.apply(pd.Series)['macro avg'].apply(pd.Series)['precision']
rep_df['val_recall'] = rep_df.class_rep_val.apply(pd.Series)['macro avg'].apply(pd.Series)['recall']
rep_df['val_f1_score'] = rep_df.class_rep_val.apply(pd.Series)['macro avg'].apply(pd.Series)['f1-score']
rep_df.drop(['class_rep_val', 'class_rep_test'], axis=1, inplace=True)
fdict = {'val_accuracy': ['mean', 'std'], 'val_precision': ['mean', 'std'],
'val_recall': ['mean', 'std'], 'val_f1_score': ['mean', 'std']}
display(rep_df.groupby(['batch_size', 'seq_length', 'learning_rate']).agg(fdict))
filename = self.name + '_report_' +\
str(np.datetime64('now')) + '.pkl'
filename = filename.replace(':', '_')
with open(filename, 'wb') as f:
pickle.dump(self.report, f)
class LinearModel(tf.keras.Model):
def __init__(self):
super().__init__()
self.flatten = layers.Flatten()
self.norm = layers.LayerNormalization()
self.dense = layers.Dense(units=17, activation='sigmoid')
def call(self, inputs, training=False):
x = self.flatten(inputs)
x = self.norm(x)
return self.dense(x)
%%time
train_df, test_df = dset.split(real=True, simul=True, drawn=True, test_size=0.2, val_size=0, sample_n=300)
tf.keras.backend.clear_session()
tf.compat.v1.reset_default_graph()
linear_param_fit = param_fit('linear_param_fit', LinearModel, flist0, 'class', categories,
[32, 64], [15, 30], [0.0001, 0.001],
train_df, test_df, 'D:/datatmp')
linear_param_fit.fit(n_splits=5, max_epochs=50)
Instances Train: 237 Test: 60 0 32 15 0.0001 189 48
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5. warnings.warn(
Epoch 1/50 2636/2636 [==============================] - 67s 25ms/step - loss: 2.3815 - categorical_accuracy: 0.3326 - precision: 0.0927 - recall: 0.7150 - f1_score: 0.1292 - val_loss: 2.0120 - val_categorical_accuracy: 0.4345 - val_precision: 0.1036 - val_recall: 0.7958 - val_f1_score: 0.1330 Epoch 2/50 2636/2636 [==============================] - 38s 14ms/step - loss: 1.7201 - categorical_accuracy: 0.4939 - precision: 0.1155 - recall: 0.8580 - f1_score: 0.1466 - val_loss: 1.7653 - val_categorical_accuracy: 0.4907 - val_precision: 0.1170 - val_recall: 0.8440 - val_f1_score: 0.1369 Epoch 3/50 2636/2636 [==============================] - 37s 14ms/step - loss: 1.5257 - categorical_accuracy: 0.5473 - precision: 0.1237 - recall: 0.8933 - f1_score: 0.1528 - val_loss: 1.6732 - val_categorical_accuracy: 0.5202 - val_precision: 0.1208 - val_recall: 0.8710 - val_f1_score: 0.1424 Epoch 4/50 2636/2636 [==============================] - 37s 14ms/step - loss: 1.4371 - categorical_accuracy: 0.5720 - precision: 0.1259 - recall: 0.9112 - f1_score: 0.1580 - val_loss: 1.6255 - val_categorical_accuracy: 0.5412 - val_precision: 0.1231 - val_recall: 0.8887 - val_f1_score: 0.1491 Epoch 5/50 2636/2636 [==============================] - 37s 14ms/step - loss: 1.3870 - categorical_accuracy: 0.5850 - precision: 0.1275 - recall: 0.9255 - f1_score: 0.1650 - val_loss: 1.6072 - val_categorical_accuracy: 0.5417 - val_precision: 0.1235 - val_recall: 0.9025 - val_f1_score: 0.1535 Epoch 6/50 2636/2636 [==============================] - 38s 14ms/step - loss: 1.3574 - categorical_accuracy: 0.5925 - precision: 0.1285 - recall: 0.9343 - f1_score: 0.1688 - val_loss: 1.5982 - val_categorical_accuracy: 0.5502 - val_precision: 0.1242 - val_recall: 0.9117 - val_f1_score: 0.1562 Epoch 7/50 2636/2636 [==============================] - 34s 13ms/step - loss: 1.3354 - categorical_accuracy: 0.5982 - precision: 0.1289 - recall: 0.9383 - f1_score: 0.1722 - val_loss: 1.5940 - val_categorical_accuracy: 0.5519 - val_precision: 0.1244 - val_recall: 0.9186 - val_f1_score: 0.1590 Epoch 8/50 2636/2636 [==============================] - 36s 14ms/step - loss: 1.3206 - categorical_accuracy: 0.6041 - precision: 0.1283 - recall: 0.9427 - f1_score: 0.1777 - val_loss: 1.5924 - val_categorical_accuracy: 0.5612 - val_precision: 0.1241 - val_recall: 0.9200 - val_f1_score: 0.1608 Epoch 9/50 2636/2636 [==============================] - 34s 13ms/step - loss: 1.3077 - categorical_accuracy: 0.6071 - precision: 0.1279 - recall: 0.9435 - f1_score: 0.1901 - val_loss: 1.5887 - val_categorical_accuracy: 0.5598 - val_precision: 0.1247 - val_recall: 0.9240 - val_f1_score: 0.1634 Epoch 10/50 2636/2636 [==============================] - 34s 13ms/step - loss: 1.2969 - categorical_accuracy: 0.6107 - precision: 0.1286 - recall: 0.9481 - f1_score: 0.1946 - val_loss: 1.5967 - val_categorical_accuracy: 0.5623 - val_precision: 0.1251 - val_recall: 0.9268 - val_f1_score: 0.1655 Epoch 11/50 2636/2636 [==============================] - 36s 13ms/step - loss: 1.2900 - categorical_accuracy: 0.6132 - precision: 0.1281 - recall: 0.9479 - f1_score: 0.1963 - val_loss: 1.5925 - val_categorical_accuracy: 0.5646 - val_precision: 0.1245 - val_recall: 0.9283 - val_f1_score: 0.1670 Epoch 12/50 2636/2636 [==============================] - 38s 14ms/step - loss: 1.2828 - categorical_accuracy: 0.6159 - precision: 0.1287 - recall: 0.9491 - f1_score: 0.1996 - val_loss: 1.5990 - val_categorical_accuracy: 0.5713 - val_precision: 0.1242 - val_recall: 0.9295 - val_f1_score: 0.1689 Epoch 13/50 2636/2636 [==============================] - 35s 13ms/step - loss: 1.2766 - categorical_accuracy: 0.6178 - precision: 0.1283 - recall: 0.9513 - f1_score: 0.2008 - val_loss: 1.5994 - val_categorical_accuracy: 0.5674 - val_precision: 0.1246 - val_recall: 0.9314 - val_f1_score: 0.1712 Epoch 14/50 2636/2636 [==============================] - 35s 13ms/step - loss: 1.2715 - categorical_accuracy: 0.6168 - precision: 0.1284 - recall: 0.9542 - f1_score: 0.2047 - val_loss: 1.6064 - val_categorical_accuracy: 0.5711 - val_precision: 0.1240 - val_recall: 0.9312 - val_f1_score: 0.1683 Epoch 15/50 2636/2636 [==============================] - 36s 13ms/step - loss: 1.2670 - categorical_accuracy: 0.6241 - precision: 0.1282 - recall: 0.9527 - f1_score: 0.2033 - val_loss: 1.6004 - val_categorical_accuracy: 0.5709 - val_precision: 0.1239 - val_recall: 0.9319 - val_f1_score: 0.1697 Epoch 16/50 2636/2636 [==============================] - 38s 14ms/step - loss: 1.2627 - categorical_accuracy: 0.6227 - precision: 0.1281 - recall: 0.9541 - f1_score: 0.2028 - val_loss: 1.6151 - val_categorical_accuracy: 0.5712 - val_precision: 0.1238 - val_recall: 0.9309 - val_f1_score: 0.1711 1 32 15 0.0001 189 48 Epoch 1/50 2643/2643 [==============================] - 40s 15ms/step - loss: 2.3237 - categorical_accuracy: 0.3533 - precision_1: 0.0969 - recall_1: 0.7423 - f1_score: 0.1342 - val_loss: 1.9285 - val_categorical_accuracy: 0.4347 - val_precision_1: 0.1125 - val_recall_1: 0.8213 - val_f1_score: 0.1478 Epoch 2/50 2643/2643 [==============================] - 39s 15ms/step - loss: 1.7316 - categorical_accuracy: 0.4959 - precision_1: 0.1203 - recall_1: 0.8641 - f1_score: 0.1528 - val_loss: 1.6516 - val_categorical_accuracy: 0.4983 - val_precision_1: 0.1224 - val_recall_1: 0.8683 - val_f1_score: 0.1561 Epoch 3/50 2643/2643 [==============================] - 39s 15ms/step - loss: 1.5417 - categorical_accuracy: 0.5460 - precision_1: 0.1279 - recall_1: 0.8963 - f1_score: 0.1626 - val_loss: 1.5326 - val_categorical_accuracy: 0.5258 - val_precision_1: 0.1279 - val_recall_1: 0.8975 - val_f1_score: 0.1631 Epoch 4/50 2643/2643 [==============================] - 39s 15ms/step - loss: 1.4535 - categorical_accuracy: 0.5672 - precision_1: 0.1304 - recall_1: 0.9154 - f1_score: 0.1702 - val_loss: 1.4740 - val_categorical_accuracy: 0.5510 - val_precision_1: 0.1285 - val_recall_1: 0.9091 - val_f1_score: 0.1701 Epoch 5/50 2643/2643 [==============================] - 40s 15ms/step - loss: 1.4066 - categorical_accuracy: 0.5803 - precision_1: 0.1321 - recall_1: 0.9251 - f1_score: 0.1762 - val_loss: 1.4341 - val_categorical_accuracy: 0.5586 - val_precision_1: 0.1297 - val_recall_1: 0.9224 - val_f1_score: 0.1742 Epoch 6/50 2643/2643 [==============================] - 40s 15ms/step - loss: 1.3761 - categorical_accuracy: 0.5891 - precision_1: 0.1313 - recall_1: 0.9331 - f1_score: 0.1772 - val_loss: 1.4058 - val_categorical_accuracy: 0.5697 - val_precision_1: 0.1312 - val_recall_1: 0.9281 - val_f1_score: 0.1808 Epoch 7/50 2643/2643 [==============================] - 39s 15ms/step - loss: 1.3565 - categorical_accuracy: 0.5987 - precision_1: 0.1319 - recall_1: 0.9349 - f1_score: 0.1834 - val_loss: 1.3932 - val_categorical_accuracy: 0.5715 - val_precision_1: 0.1314 - val_recall_1: 0.9333 - val_f1_score: 0.1857 Epoch 8/50 2643/2643 [==============================] - 38s 14ms/step - loss: 1.3410 - categorical_accuracy: 0.6027 - precision_1: 0.1316 - recall_1: 0.9379 - f1_score: 0.1879 - val_loss: 1.3837 - val_categorical_accuracy: 0.5725 - val_precision_1: 0.1309 - val_recall_1: 0.9393 - val_f1_score: 0.1908 Epoch 9/50 2643/2643 [==============================] - 38s 14ms/step - loss: 1.3294 - categorical_accuracy: 0.6050 - precision_1: 0.1313 - recall_1: 0.9400 - f1_score: 0.1901 - val_loss: 1.3671 - val_categorical_accuracy: 0.5860 - val_precision_1: 0.1317 - val_recall_1: 0.9417 - val_f1_score: 0.1938 Epoch 10/50 2643/2643 [==============================] - 38s 14ms/step - loss: 1.3198 - categorical_accuracy: 0.6123 - precision_1: 0.1317 - recall_1: 0.9414 - f1_score: 0.1953 - val_loss: 1.3612 - val_categorical_accuracy: 0.5833 - val_precision_1: 0.1316 - val_recall_1: 0.9441 - val_f1_score: 0.1979 Epoch 11/50 2643/2643 [==============================] - 39s 15ms/step - loss: 1.3114 - categorical_accuracy: 0.6138 - precision_1: 0.1315 - recall_1: 0.9423 - f1_score: 0.1984 - val_loss: 1.3536 - val_categorical_accuracy: 0.5872 - val_precision_1: 0.1324 - val_recall_1: 0.9439 - val_f1_score: 0.1979 Epoch 12/50 2643/2643 [==============================] - 39s 15ms/step - loss: 1.3049 - categorical_accuracy: 0.6155 - precision_1: 0.1316 - recall_1: 0.9433 - f1_score: 0.2004 - val_loss: 1.3461 - val_categorical_accuracy: 0.5956 - val_precision_1: 0.1330 - val_recall_1: 0.9479 - val_f1_score: 0.1996 Epoch 13/50 2643/2643 [==============================] - 38s 14ms/step - loss: 1.2993 - categorical_accuracy: 0.6191 - precision_1: 0.1316 - recall_1: 0.9450 - f1_score: 0.2107 - val_loss: 1.3434 - val_categorical_accuracy: 0.5985 - val_precision_1: 0.1324 - val_recall_1: 0.9470 - val_f1_score: 0.1965 Epoch 14/50 2643/2643 [==============================] - 39s 15ms/step - loss: 1.2939 - categorical_accuracy: 0.6196 - precision_1: 0.1315 - recall_1: 0.9465 - f1_score: 0.2120 - val_loss: 1.3467 - val_categorical_accuracy: 0.5970 - val_precision_1: 0.1321 - val_recall_1: 0.9447 - val_f1_score: 0.1967 Epoch 15/50 2643/2643 [==============================] - 39s 15ms/step - loss: 1.2904 - categorical_accuracy: 0.6204 - precision_1: 0.1314 - recall_1: 0.9457 - f1_score: 0.2089 - val_loss: 1.3481 - val_categorical_accuracy: 0.5973 - val_precision_1: 0.1341 - val_recall_1: 0.9493 - val_f1_score: 0.2015 Epoch 16/50 2643/2643 [==============================] - 37s 14ms/step - loss: 1.2873 - categorical_accuracy: 0.6218 - precision_1: 0.1321 - recall_1: 0.9474 - f1_score: 0.2218 - val_loss: 1.3448 - val_categorical_accuracy: 0.5925 - val_precision_1: 0.1337 - val_recall_1: 0.9520 - val_f1_score: 0.2004 Epoch 17/50 2643/2643 [==============================] - 36s 14ms/step - loss: 1.2827 - categorical_accuracy: 0.6257 - precision_1: 0.1324 - recall_1: 0.9474 - f1_score: 0.2211 - val_loss: 1.3332 - val_categorical_accuracy: 0.5996 - val_precision_1: 0.1328 - val_recall_1: 0.9561 - val_f1_score: 0.1991 Epoch 18/50 2643/2643 [==============================] - 36s 14ms/step - loss: 1.2801 - categorical_accuracy: 0.6227 - precision_1: 0.1318 - recall_1: 0.9488 - f1_score: 0.2263 - val_loss: 1.3315 - val_categorical_accuracy: 0.6069 - val_precision_1: 0.1331 - val_recall_1: 0.9540 - val_f1_score: 0.1990 2 32 15 0.0001 190 47 Epoch 1/50 2512/2512 [==============================] - 38s 15ms/step - loss: 2.3215 - categorical_accuracy: 0.3605 - precision_2: 0.1058 - recall_2: 0.7259 - f1_score: 0.1380 - val_loss: 2.1867 - val_categorical_accuracy: 0.3474 - val_precision_2: 0.1092 - val_recall_2: 0.7627 - val_f1_score: 0.1481 Epoch 2/50 2512/2512 [==============================] - 36s 14ms/step - loss: 1.7180 - categorical_accuracy: 0.5087 - precision_2: 0.1303 - recall_2: 0.8434 - f1_score: 0.1558 - val_loss: 2.0351 - val_categorical_accuracy: 0.4213 - val_precision_2: 0.1197 - val_recall_2: 0.8092 - val_f1_score: 0.1512 Epoch 3/50 2512/2512 [==============================] - 38s 15ms/step - loss: 1.5233 - categorical_accuracy: 0.5557 - precision_2: 0.1404 - recall_2: 0.8801 - f1_score: 0.1627 - val_loss: 2.0132 - val_categorical_accuracy: 0.4407 - val_precision_2: 0.1292 - val_recall_2: 0.8300 - val_f1_score: 0.1553 Epoch 4/50 2512/2512 [==============================] - 39s 16ms/step - loss: 1.4320 - categorical_accuracy: 0.5767 - precision_2: 0.1445 - recall_2: 0.8993 - f1_score: 0.1711 - val_loss: 2.0195 - val_categorical_accuracy: 0.4493 - val_precision_2: 0.1293 - val_recall_2: 0.8455 - val_f1_score: 0.1576 Epoch 5/50 2512/2512 [==============================] - 35s 14ms/step - loss: 1.3809 - categorical_accuracy: 0.5854 - precision_2: 0.1427 - recall_2: 0.9158 - f1_score: 0.1778 - val_loss: 2.0388 - val_categorical_accuracy: 0.4501 - val_precision_2: 0.1311 - val_recall_2: 0.8597 - val_f1_score: 0.1614 Epoch 6/50 2512/2512 [==============================] - 34s 14ms/step - loss: 1.3504 - categorical_accuracy: 0.5930 - precision_2: 0.1431 - recall_2: 0.9291 - f1_score: 0.1878 - val_loss: 2.0476 - val_categorical_accuracy: 0.4481 - val_precision_2: 0.1308 - val_recall_2: 0.8654 - val_f1_score: 0.1636 Epoch 7/50 2512/2512 [==============================] - 38s 15ms/step - loss: 1.3281 - categorical_accuracy: 0.5954 - precision_2: 0.1427 - recall_2: 0.9347 - f1_score: 0.1934 - val_loss: 2.0732 - val_categorical_accuracy: 0.4622 - val_precision_2: 0.1282 - val_recall_2: 0.8680 - val_f1_score: 0.1617 Epoch 8/50 2512/2512 [==============================] - 36s 14ms/step - loss: 1.3116 - categorical_accuracy: 0.6054 - precision_2: 0.1417 - recall_2: 0.9390 - f1_score: 0.1992 - val_loss: 2.1081 - val_categorical_accuracy: 0.4592 - val_precision_2: 0.1273 - val_recall_2: 0.8712 - val_f1_score: 0.1611 Epoch 9/50 2512/2512 [==============================] - 36s 14ms/step - loss: 1.2999 - categorical_accuracy: 0.6060 - precision_2: 0.1410 - recall_2: 0.9427 - f1_score: 0.2026 - val_loss: 2.1299 - val_categorical_accuracy: 0.4626 - val_precision_2: 0.1264 - val_recall_2: 0.8723 - val_f1_score: 0.1616 3 32 15 0.0001 190 47 Epoch 1/50 2678/2678 [==============================] - 37s 13ms/step - loss: 2.3113 - categorical_accuracy: 0.3426 - precision_3: 0.0989 - recall_3: 0.7614 - f1_score: 0.1403 - val_loss: 1.8747 - val_categorical_accuracy: 0.4779 - val_precision_3: 0.1108 - val_recall_3: 0.8228 - val_f1_score: 0.1435 Epoch 2/50 2678/2678 [==============================] - 34s 13ms/step - loss: 1.7457 - categorical_accuracy: 0.5058 - precision_3: 0.1144 - recall_3: 0.8578 - f1_score: 0.1497 - val_loss: 1.6260 - val_categorical_accuracy: 0.5439 - val_precision_3: 0.1198 - val_recall_3: 0.8687 - val_f1_score: 0.1469 Epoch 3/50 2678/2678 [==============================] - 38s 14ms/step - loss: 1.5587 - categorical_accuracy: 0.5511 - precision_3: 0.1232 - recall_3: 0.8901 - f1_score: 0.1554 - val_loss: 1.5118 - val_categorical_accuracy: 0.5673 - val_precision_3: 0.1250 - val_recall_3: 0.8961 - val_f1_score: 0.1534 Epoch 4/50 2678/2678 [==============================] - 37s 14ms/step - loss: 1.4649 - categorical_accuracy: 0.5734 - precision_3: 0.1269 - recall_3: 0.9123 - f1_score: 0.1614 - val_loss: 1.4626 - val_categorical_accuracy: 0.5765 - val_precision_3: 0.1266 - val_recall_3: 0.9106 - val_f1_score: 0.1584 Epoch 5/50 2678/2678 [==============================] - 35s 13ms/step - loss: 1.4102 - categorical_accuracy: 0.5821 - precision_3: 0.1282 - recall_3: 0.9258 - f1_score: 0.1668 - val_loss: 1.4252 - val_categorical_accuracy: 0.5884 - val_precision_3: 0.1281 - val_recall_3: 0.9242 - val_f1_score: 0.1659 Epoch 6/50 2678/2678 [==============================] - 38s 14ms/step - loss: 1.3806 - categorical_accuracy: 0.5901 - precision_3: 0.1293 - recall_3: 0.9368 - f1_score: 0.1775 - val_loss: 1.4033 - val_categorical_accuracy: 0.5984 - val_precision_3: 0.1289 - val_recall_3: 0.9305 - val_f1_score: 0.1694 Epoch 7/50 2678/2678 [==============================] - 37s 14ms/step - loss: 1.3577 - categorical_accuracy: 0.5957 - precision_3: 0.1296 - recall_3: 0.9399 - f1_score: 0.1845 - val_loss: 1.3915 - val_categorical_accuracy: 0.6071 - val_precision_3: 0.1291 - val_recall_3: 0.9339 - val_f1_score: 0.1731 Epoch 8/50 2678/2678 [==============================] - 36s 13ms/step - loss: 1.3404 - categorical_accuracy: 0.5984 - precision_3: 0.1305 - recall_3: 0.9472 - f1_score: 0.1896 - val_loss: 1.3898 - val_categorical_accuracy: 0.6094 - val_precision_3: 0.1283 - val_recall_3: 0.9348 - val_f1_score: 0.1742 Epoch 9/50 2678/2678 [==============================] - 36s 13ms/step - loss: 1.3281 - categorical_accuracy: 0.6017 - precision_3: 0.1299 - recall_3: 0.9509 - f1_score: 0.1928 - val_loss: 1.3802 - val_categorical_accuracy: 0.6149 - val_precision_3: 0.1286 - val_recall_3: 0.9349 - val_f1_score: 0.1756 Epoch 10/50 2678/2678 [==============================] - 35s 13ms/step - loss: 1.3168 - categorical_accuracy: 0.6060 - precision_3: 0.1304 - recall_3: 0.9508 - f1_score: 0.2000 - val_loss: 1.3862 - val_categorical_accuracy: 0.5973 - val_precision_3: 0.1276 - val_recall_3: 0.9365 - val_f1_score: 0.1765 Epoch 11/50 2678/2678 [==============================] - 37s 14ms/step - loss: 1.3105 - categorical_accuracy: 0.6085 - precision_3: 0.1296 - recall_3: 0.9545 - f1_score: 0.1989 - val_loss: 1.3797 - val_categorical_accuracy: 0.6041 - val_precision_3: 0.1288 - val_recall_3: 0.9374 - val_f1_score: 0.1797 Epoch 12/50 2678/2678 [==============================] - 36s 14ms/step - loss: 1.3026 - categorical_accuracy: 0.6145 - precision_3: 0.1305 - recall_3: 0.9549 - f1_score: 0.2041 - val_loss: 1.3720 - val_categorical_accuracy: 0.6102 - val_precision_3: 0.1286 - val_recall_3: 0.9394 - val_f1_score: 0.1826 Epoch 13/50 2678/2678 [==============================] - 36s 13ms/step - loss: 1.2975 - categorical_accuracy: 0.6105 - precision_3: 0.1304 - recall_3: 0.9559 - f1_score: 0.2057 - val_loss: 1.3590 - val_categorical_accuracy: 0.6209 - val_precision_3: 0.1282 - val_recall_3: 0.9396 - val_f1_score: 0.1824 Epoch 14/50 2678/2678 [==============================] - 39s 14ms/step - loss: 1.2923 - categorical_accuracy: 0.6169 - precision_3: 0.1299 - recall_3: 0.9567 - f1_score: 0.2068 - val_loss: 1.3622 - val_categorical_accuracy: 0.6148 - val_precision_3: 0.1289 - val_recall_3: 0.9401 - val_f1_score: 0.1845 Epoch 15/50 2678/2678 [==============================] - 36s 13ms/step - loss: 1.2877 - categorical_accuracy: 0.6164 - precision_3: 0.1300 - recall_3: 0.9579 - f1_score: 0.2104 - val_loss: 1.3626 - val_categorical_accuracy: 0.6193 - val_precision_3: 0.1284 - val_recall_3: 0.9407 - val_f1_score: 0.1848 Epoch 16/50 2678/2678 [==============================] - 35s 13ms/step - loss: 1.2844 - categorical_accuracy: 0.6153 - precision_3: 0.1302 - recall_3: 0.9586 - f1_score: 0.2144 - val_loss: 1.3566 - val_categorical_accuracy: 0.6202 - val_precision_3: 0.1276 - val_recall_3: 0.9410 - val_f1_score: 0.1860 Epoch 17/50 2678/2678 [==============================] - 36s 14ms/step - loss: 1.2805 - categorical_accuracy: 0.6184 - precision_3: 0.1301 - recall_3: 0.9579 - f1_score: 0.2160 - val_loss: 1.3535 - val_categorical_accuracy: 0.6272 - val_precision_3: 0.1279 - val_recall_3: 0.9406 - val_f1_score: 0.1855 Epoch 18/50 2678/2678 [==============================] - 36s 14ms/step - loss: 1.2770 - categorical_accuracy: 0.6206 - precision_3: 0.1300 - recall_3: 0.9591 - f1_score: 0.2179 - val_loss: 1.3495 - val_categorical_accuracy: 0.6273 - val_precision_3: 0.1279 - val_recall_3: 0.9414 - val_f1_score: 0.1871 Epoch 19/50 2678/2678 [==============================] - 36s 13ms/step - loss: 1.2730 - categorical_accuracy: 0.6239 - precision_3: 0.1299 - recall_3: 0.9606 - f1_score: 0.2195 - val_loss: 1.3590 - val_categorical_accuracy: 0.6203 - val_precision_3: 0.1281 - val_recall_3: 0.9420 - val_f1_score: 0.1884 Epoch 20/50 2678/2678 [==============================] - 36s 13ms/step - loss: 1.2711 - categorical_accuracy: 0.6275 - precision_3: 0.1299 - recall_3: 0.9590 - f1_score: 0.2211 - val_loss: 1.3524 - val_categorical_accuracy: 0.6211 - val_precision_3: 0.1278 - val_recall_3: 0.9427 - val_f1_score: 0.1874 Epoch 21/50 2678/2678 [==============================] - 37s 14ms/step - loss: 1.2692 - categorical_accuracy: 0.6220 - precision_3: 0.1303 - recall_3: 0.9604 - f1_score: 0.2203 - val_loss: 1.3525 - val_categorical_accuracy: 0.6202 - val_precision_3: 0.1273 - val_recall_3: 0.9417 - val_f1_score: 0.1879 Epoch 22/50 2678/2678 [==============================] - 38s 14ms/step - loss: 1.2641 - categorical_accuracy: 0.6277 - precision_3: 0.1299 - recall_3: 0.9621 - f1_score: 0.2231 - val_loss: 1.3638 - val_categorical_accuracy: 0.6085 - val_precision_3: 0.1279 - val_recall_3: 0.9426 - val_f1_score: 0.1873 4 32 15 0.0001 190 47 Epoch 1/50 2679/2679 [==============================] - 44s 16ms/step - loss: 2.3235 - categorical_accuracy: 0.3493 - precision_4: 0.1024 - recall_4: 0.7580 - f1_score: 0.1444 - val_loss: 1.8035 - val_categorical_accuracy: 0.4754 - val_precision_4: 0.1232 - val_recall_4: 0.8676 - val_f1_score: 0.1528 Epoch 2/50 2679/2679 [==============================] - 37s 14ms/step - loss: 1.7599 - categorical_accuracy: 0.4851 - precision_4: 0.1230 - recall_4: 0.8642 - f1_score: 0.1570 - val_loss: 1.5527 - val_categorical_accuracy: 0.5452 - val_precision_4: 0.1338 - val_recall_4: 0.9052 - val_f1_score: 0.1553 Epoch 3/50 2679/2679 [==============================] - 36s 14ms/step - loss: 1.5688 - categorical_accuracy: 0.5395 - precision_4: 0.1332 - recall_4: 0.8984 - f1_score: 0.1650 - val_loss: 1.4485 - val_categorical_accuracy: 0.5708 - val_precision_4: 0.1380 - val_recall_4: 0.9268 - val_f1_score: 0.1623 Epoch 4/50 2679/2679 [==============================] - 38s 14ms/step - loss: 1.4790 - categorical_accuracy: 0.5609 - precision_4: 0.1344 - recall_4: 0.9203 - f1_score: 0.1746 - val_loss: 1.4054 - val_categorical_accuracy: 0.5811 - val_precision_4: 0.1351 - val_recall_4: 0.9297 - val_f1_score: 0.1694 Epoch 5/50 2679/2679 [==============================] - 38s 14ms/step - loss: 1.4292 - categorical_accuracy: 0.5731 - precision_4: 0.1344 - recall_4: 0.9296 - f1_score: 0.1816 - val_loss: 1.3824 - val_categorical_accuracy: 0.5903 - val_precision_4: 0.1349 - val_recall_4: 0.9348 - val_f1_score: 0.1721 Epoch 6/50 2679/2679 [==============================] - 38s 14ms/step - loss: 1.3994 - categorical_accuracy: 0.5834 - precision_4: 0.1343 - recall_4: 0.9368 - f1_score: 0.1884 - val_loss: 1.3564 - val_categorical_accuracy: 0.5966 - val_precision_4: 0.1341 - val_recall_4: 0.9405 - val_f1_score: 0.1754 Epoch 7/50 2679/2679 [==============================] - 39s 14ms/step - loss: 1.3757 - categorical_accuracy: 0.5890 - precision_4: 0.1340 - recall_4: 0.9431 - f1_score: 0.1848 - val_loss: 1.3402 - val_categorical_accuracy: 0.6027 - val_precision_4: 0.1354 - val_recall_4: 0.9404 - val_f1_score: 0.1763 Epoch 8/50 2679/2679 [==============================] - 40s 15ms/step - loss: 1.3600 - categorical_accuracy: 0.5912 - precision_4: 0.1347 - recall_4: 0.9453 - f1_score: 0.1960 - val_loss: 1.3342 - val_categorical_accuracy: 0.6048 - val_precision_4: 0.1350 - val_recall_4: 0.9474 - val_f1_score: 0.1781 Epoch 9/50 2679/2679 [==============================] - 40s 15ms/step - loss: 1.3471 - categorical_accuracy: 0.5986 - precision_4: 0.1349 - recall_4: 0.9474 - f1_score: 0.2066 - val_loss: 1.3227 - val_categorical_accuracy: 0.6069 - val_precision_4: 0.1340 - val_recall_4: 0.9499 - val_f1_score: 0.1785 Epoch 10/50 2679/2679 [==============================] - 39s 15ms/step - loss: 1.3367 - categorical_accuracy: 0.6031 - precision_4: 0.1344 - recall_4: 0.9493 - f1_score: 0.2031 - val_loss: 1.3193 - val_categorical_accuracy: 0.6114 - val_precision_4: 0.1330 - val_recall_4: 0.9448 - val_f1_score: 0.1781 Epoch 11/50 2679/2679 [==============================] - 34s 13ms/step - loss: 1.3281 - categorical_accuracy: 0.6036 - precision_4: 0.1337 - recall_4: 0.9512 - f1_score: 0.2132 - val_loss: 1.3106 - val_categorical_accuracy: 0.6213 - val_precision_4: 0.1343 - val_recall_4: 0.9447 - val_f1_score: 0.1808 Epoch 12/50 2679/2679 [==============================] - 38s 14ms/step - loss: 1.3222 - categorical_accuracy: 0.6073 - precision_4: 0.1344 - recall_4: 0.9511 - f1_score: 0.2070 - val_loss: 1.3039 - val_categorical_accuracy: 0.6178 - val_precision_4: 0.1342 - val_recall_4: 0.9472 - val_f1_score: 0.1845 Epoch 13/50 2679/2679 [==============================] - 37s 14ms/step - loss: 1.3153 - categorical_accuracy: 0.6098 - precision_4: 0.1341 - recall_4: 0.9523 - f1_score: 0.2172 - val_loss: 1.3017 - val_categorical_accuracy: 0.6184 - val_precision_4: 0.1332 - val_recall_4: 0.9507 - val_f1_score: 0.1867 Epoch 14/50 2679/2679 [==============================] - 35s 13ms/step - loss: 1.3102 - categorical_accuracy: 0.6126 - precision_4: 0.1337 - recall_4: 0.9520 - f1_score: 0.2128 - val_loss: 1.2943 - val_categorical_accuracy: 0.6276 - val_precision_4: 0.1349 - val_recall_4: 0.9508 - val_f1_score: 0.1880 Epoch 15/50 2679/2679 [==============================] - 43s 16ms/step - loss: 1.3049 - categorical_accuracy: 0.6120 - precision_4: 0.1339 - recall_4: 0.9532 - f1_score: 0.2226 - val_loss: 1.3022 - val_categorical_accuracy: 0.6224 - val_precision_4: 0.1338 - val_recall_4: 0.9537 - val_f1_score: 0.1873 Epoch 16/50 2679/2679 [==============================] - 38s 14ms/step - loss: 1.3021 - categorical_accuracy: 0.6172 - precision_4: 0.1344 - recall_4: 0.9542 - f1_score: 0.2267 - val_loss: 1.2889 - val_categorical_accuracy: 0.6250 - val_precision_4: 0.1347 - val_recall_4: 0.9538 - val_f1_score: 0.1878 Epoch 17/50 2679/2679 [==============================] - 37s 14ms/step - loss: 1.2981 - categorical_accuracy: 0.6183 - precision_4: 0.1340 - recall_4: 0.9544 - f1_score: 0.2301 - val_loss: 1.2904 - val_categorical_accuracy: 0.6200 - val_precision_4: 0.1336 - val_recall_4: 0.9502 - val_f1_score: 0.1872 0 32 15 0.001 189 48 Epoch 1/50
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5. warnings.warn(
2636/2636 [==============================] - 42s 16ms/step - loss: 1.6167 - categorical_accuracy: 0.5376 - precision_5: 0.1241 - recall_5: 0.8977 - f1_score: 0.1576 - val_loss: 1.5686 - val_categorical_accuracy: 0.5692 - val_precision_5: 0.1290 - val_recall_5: 0.9283 - val_f1_score: 0.1594 Epoch 2/50 2636/2636 [==============================] - 39s 15ms/step - loss: 1.3654 - categorical_accuracy: 0.5945 - precision_5: 0.1290 - recall_5: 0.9491 - f1_score: 0.1813 - val_loss: 1.5518 - val_categorical_accuracy: 0.5455 - val_precision_5: 0.1230 - val_recall_5: 0.9326 - val_f1_score: 0.1675 Epoch 3/50 2636/2636 [==============================] - 40s 15ms/step - loss: 1.3211 - categorical_accuracy: 0.6177 - precision_5: 0.1284 - recall_5: 0.9554 - f1_score: 0.1901 - val_loss: 1.6260 - val_categorical_accuracy: 0.5621 - val_precision_5: 0.1239 - val_recall_5: 0.9286 - val_f1_score: 0.1765 Epoch 4/50 2636/2636 [==============================] - 37s 14ms/step - loss: 1.3152 - categorical_accuracy: 0.6109 - precision_5: 0.1283 - recall_5: 0.9572 - f1_score: 0.1971 - val_loss: 1.5842 - val_categorical_accuracy: 0.5486 - val_precision_5: 0.1235 - val_recall_5: 0.9306 - val_f1_score: 0.1736 Epoch 5/50 2636/2636 [==============================] - 40s 15ms/step - loss: 1.3028 - categorical_accuracy: 0.6187 - precision_5: 0.1281 - recall_5: 0.9593 - f1_score: 0.2018 - val_loss: 1.6329 - val_categorical_accuracy: 0.5534 - val_precision_5: 0.1256 - val_recall_5: 0.9193 - val_f1_score: 0.1783 Epoch 6/50 2636/2636 [==============================] - 36s 13ms/step - loss: 1.2920 - categorical_accuracy: 0.6262 - precision_5: 0.1300 - recall_5: 0.9584 - f1_score: 0.2049 - val_loss: 1.5742 - val_categorical_accuracy: 0.5747 - val_precision_5: 0.1215 - val_recall_5: 0.9303 - val_f1_score: 0.1723 Epoch 7/50 2636/2636 [==============================] - 41s 16ms/step - loss: 1.2820 - categorical_accuracy: 0.6260 - precision_5: 0.1305 - recall_5: 0.9562 - f1_score: 0.2090 - val_loss: 1.5755 - val_categorical_accuracy: 0.5829 - val_precision_5: 0.1264 - val_recall_5: 0.9332 - val_f1_score: 0.1857 Epoch 8/50 2636/2636 [==============================] - 40s 15ms/step - loss: 1.2745 - categorical_accuracy: 0.6346 - precision_5: 0.1316 - recall_5: 0.9565 - f1_score: 0.2145 - val_loss: 1.6308 - val_categorical_accuracy: 0.5762 - val_precision_5: 0.1261 - val_recall_5: 0.9228 - val_f1_score: 0.1772 Epoch 9/50 2636/2636 [==============================] - 36s 14ms/step - loss: 1.2715 - categorical_accuracy: 0.6337 - precision_5: 0.1322 - recall_5: 0.9559 - f1_score: 0.2159 - val_loss: 1.6406 - val_categorical_accuracy: 0.5852 - val_precision_5: 0.1262 - val_recall_5: 0.9209 - val_f1_score: 0.1785 Epoch 10/50 2636/2636 [==============================] - 47s 18ms/step - loss: 1.2650 - categorical_accuracy: 0.6351 - precision_5: 0.1339 - recall_5: 0.9543 - f1_score: 0.2197 - val_loss: 1.6375 - val_categorical_accuracy: 0.5703 - val_precision_5: 0.1267 - val_recall_5: 0.9258 - val_f1_score: 0.1820 1 32 15 0.001 189 48 Epoch 1/50 2643/2643 [==============================] - 39s 14ms/step - loss: 1.6313 - categorical_accuracy: 0.5340 - precision_6: 0.1335 - recall_6: 0.8912 - f1_score: 0.1693 - val_loss: 1.4402 - val_categorical_accuracy: 0.5769 - val_precision_6: 0.1439 - val_recall_6: 0.9371 - val_f1_score: 0.1882 Epoch 2/50 2643/2643 [==============================] - 38s 14ms/step - loss: 1.3920 - categorical_accuracy: 0.5822 - precision_6: 0.1356 - recall_6: 0.9475 - f1_score: 0.2016 - val_loss: 1.4028 - val_categorical_accuracy: 0.5565 - val_precision_6: 0.1375 - val_recall_6: 0.9493 - val_f1_score: 0.2137 Epoch 3/50 2643/2643 [==============================] - 38s 14ms/step - loss: 1.3664 - categorical_accuracy: 0.5921 - precision_6: 0.1339 - recall_6: 0.9496 - f1_score: 0.2227 - val_loss: 1.3373 - val_categorical_accuracy: 0.6184 - val_precision_6: 0.1382 - val_recall_6: 0.9648 - val_f1_score: 0.2118 Epoch 4/50 2643/2643 [==============================] - 38s 15ms/step - loss: 1.3431 - categorical_accuracy: 0.6036 - precision_6: 0.1337 - recall_6: 0.9511 - f1_score: 0.2400 - val_loss: 1.3674 - val_categorical_accuracy: 0.6170 - val_precision_6: 0.1358 - val_recall_6: 0.9611 - val_f1_score: 0.2005 Epoch 5/50 2643/2643 [==============================] - 38s 14ms/step - loss: 1.3281 - categorical_accuracy: 0.6116 - precision_6: 0.1335 - recall_6: 0.9529 - f1_score: 0.2327 - val_loss: 1.3767 - val_categorical_accuracy: 0.5867 - val_precision_6: 0.1367 - val_recall_6: 0.9548 - val_f1_score: 0.2090 2 32 15 0.001 190 47 Epoch 1/50 2512/2512 [==============================] - 39s 15ms/step - loss: 1.5887 - categorical_accuracy: 0.5373 - precision_7: 0.1307 - recall_7: 0.8887 - f1_score: 0.1596 - val_loss: 2.1039 - val_categorical_accuracy: 0.4683 - val_precision_7: 0.1167 - val_recall_7: 0.8594 - val_f1_score: 0.1496 Epoch 2/50 2512/2512 [==============================] - 38s 15ms/step - loss: 1.3432 - categorical_accuracy: 0.5968 - precision_7: 0.1324 - recall_7: 0.9510 - f1_score: 0.1919 - val_loss: 2.1386 - val_categorical_accuracy: 0.4801 - val_precision_7: 0.1240 - val_recall_7: 0.8848 - val_f1_score: 0.1637 Epoch 3/50 2512/2512 [==============================] - 38s 15ms/step - loss: 1.3180 - categorical_accuracy: 0.6043 - precision_7: 0.1316 - recall_7: 0.9548 - f1_score: 0.2023 - val_loss: 2.0110 - val_categorical_accuracy: 0.4772 - val_precision_7: 0.1227 - val_recall_7: 0.9162 - val_f1_score: 0.1624 Epoch 4/50 2512/2512 [==============================] - 38s 15ms/step - loss: 1.3014 - categorical_accuracy: 0.6139 - precision_7: 0.1305 - recall_7: 0.9537 - f1_score: 0.2097 - val_loss: 2.1472 - val_categorical_accuracy: 0.4650 - val_precision_7: 0.1259 - val_recall_7: 0.9185 - val_f1_score: 0.1640 Epoch 5/50 2512/2512 [==============================] - 38s 15ms/step - loss: 1.2976 - categorical_accuracy: 0.6114 - precision_7: 0.1315 - recall_7: 0.9616 - f1_score: 0.2218 - val_loss: 2.2125 - val_categorical_accuracy: 0.4840 - val_precision_7: 0.1240 - val_recall_7: 0.8964 - val_f1_score: 0.1653 Epoch 6/50 2512/2512 [==============================] - 38s 15ms/step - loss: 1.2838 - categorical_accuracy: 0.6262 - precision_7: 0.1323 - recall_7: 0.9542 - f1_score: 0.2187 - val_loss: 2.2631 - val_categorical_accuracy: 0.4479 - val_precision_7: 0.1253 - val_recall_7: 0.9262 - val_f1_score: 0.1640 Epoch 7/50 2512/2512 [==============================] - 38s 15ms/step - loss: 1.2738 - categorical_accuracy: 0.6264 - precision_7: 0.1329 - recall_7: 0.9557 - f1_score: 0.2242 - val_loss: 2.1963 - val_categorical_accuracy: 0.4650 - val_precision_7: 0.1227 - val_recall_7: 0.9334 - val_f1_score: 0.1579 Epoch 8/50 2512/2512 [==============================] - 38s 15ms/step - loss: 1.2742 - categorical_accuracy: 0.6246 - precision_7: 0.1327 - recall_7: 0.9554 - f1_score: 0.2293 - val_loss: 2.4059 - val_categorical_accuracy: 0.4664 - val_precision_7: 0.1278 - val_recall_7: 0.8853 - val_f1_score: 0.1684 Epoch 9/50 2512/2512 [==============================] - 39s 15ms/step - loss: 1.2609 - categorical_accuracy: 0.6354 - precision_7: 0.1364 - recall_7: 0.9576 - f1_score: 0.2281 - val_loss: 2.2724 - val_categorical_accuracy: 0.4759 - val_precision_7: 0.1226 - val_recall_7: 0.8977 - val_f1_score: 0.1687 Epoch 10/50 2512/2512 [==============================] - 38s 15ms/step - loss: 1.2659 - categorical_accuracy: 0.6335 - precision_7: 0.1351 - recall_7: 0.9541 - f1_score: 0.2368 - val_loss: 2.3369 - val_categorical_accuracy: 0.4662 - val_precision_7: 0.1261 - val_recall_7: 0.9206 - val_f1_score: 0.1678 Epoch 11/50 2512/2512 [==============================] - 38s 15ms/step - loss: 1.2613 - categorical_accuracy: 0.6197 - precision_7: 0.1360 - recall_7: 0.9534 - f1_score: 0.2392 - val_loss: 2.3875 - val_categorical_accuracy: 0.4774 - val_precision_7: 0.1268 - val_recall_7: 0.9026 - val_f1_score: 0.1672 Epoch 12/50 2512/2512 [==============================] - 38s 15ms/step - loss: 1.2585 - categorical_accuracy: 0.6349 - precision_7: 0.1373 - recall_7: 0.9466 - f1_score: 0.2324 - val_loss: 2.3412 - val_categorical_accuracy: 0.4795 - val_precision_7: 0.1310 - val_recall_7: 0.9275 - val_f1_score: 0.1720 Epoch 13/50 2512/2512 [==============================] - 38s 15ms/step - loss: 1.2517 - categorical_accuracy: 0.6364 - precision_7: 0.1381 - recall_7: 0.9481 - f1_score: 0.2405 - val_loss: 2.3580 - val_categorical_accuracy: 0.4568 - val_precision_7: 0.1239 - val_recall_7: 0.8737 - val_f1_score: 0.1670 Epoch 14/50 2512/2512 [==============================] - 38s 15ms/step - loss: 1.2578 - categorical_accuracy: 0.6328 - precision_7: 0.1385 - recall_7: 0.9484 - f1_score: 0.2423 - val_loss: 2.4306 - val_categorical_accuracy: 0.4771 - val_precision_7: 0.1264 - val_recall_7: 0.8778 - val_f1_score: 0.1685 Epoch 15/50 2512/2512 [==============================] - 38s 15ms/step - loss: 1.2458 - categorical_accuracy: 0.6374 - precision_7: 0.1401 - recall_7: 0.9481 - f1_score: 0.2442 - val_loss: 2.4108 - val_categorical_accuracy: 0.4762 - val_precision_7: 0.1251 - val_recall_7: 0.8668 - val_f1_score: 0.1689 3 32 15 0.001 190 47 Epoch 1/50 2678/2678 [==============================] - 39s 14ms/step - loss: 1.6369 - categorical_accuracy: 0.5218 - precision_8: 0.1288 - recall_8: 0.8985 - f1_score: 0.1658 - val_loss: 1.5153 - val_categorical_accuracy: 0.5301 - val_precision_8: 0.1305 - val_recall_8: 0.9404 - val_f1_score: 0.1878 Epoch 2/50 2678/2678 [==============================] - 37s 14ms/step - loss: 1.3784 - categorical_accuracy: 0.5848 - precision_8: 0.1323 - recall_8: 0.9605 - f1_score: 0.2017 - val_loss: 1.4893 - val_categorical_accuracy: 0.5445 - val_precision_8: 0.1332 - val_recall_8: 0.9482 - val_f1_score: 0.1977 Epoch 3/50 2678/2678 [==============================] - 37s 14ms/step - loss: 1.3507 - categorical_accuracy: 0.5973 - precision_8: 0.1320 - recall_8: 0.9603 - f1_score: 0.2099 - val_loss: 1.4097 - val_categorical_accuracy: 0.6045 - val_precision_8: 0.1304 - val_recall_8: 0.9398 - val_f1_score: 0.1932 Epoch 4/50 2678/2678 [==============================] - 37s 14ms/step - loss: 1.3271 - categorical_accuracy: 0.5998 - precision_8: 0.1334 - recall_8: 0.9651 - f1_score: 0.2253 - val_loss: 1.4649 - val_categorical_accuracy: 0.5762 - val_precision_8: 0.1298 - val_recall_8: 0.9526 - val_f1_score: 0.1955 Epoch 5/50 2678/2678 [==============================] - 37s 14ms/step - loss: 1.3228 - categorical_accuracy: 0.6064 - precision_8: 0.1327 - recall_8: 0.9642 - f1_score: 0.2246 - val_loss: 1.3844 - val_categorical_accuracy: 0.6128 - val_precision_8: 0.1353 - val_recall_8: 0.9518 - val_f1_score: 0.2019 Epoch 6/50 2678/2678 [==============================] - 37s 14ms/step - loss: 1.3115 - categorical_accuracy: 0.6123 - precision_8: 0.1341 - recall_8: 0.9660 - f1_score: 0.2345 - val_loss: 1.3734 - val_categorical_accuracy: 0.6127 - val_precision_8: 0.1314 - val_recall_8: 0.9455 - val_f1_score: 0.1959 Epoch 7/50 2678/2678 [==============================] - 37s 14ms/step - loss: 1.3080 - categorical_accuracy: 0.6122 - precision_8: 0.1356 - recall_8: 0.9639 - f1_score: 0.2428 - val_loss: 1.3344 - val_categorical_accuracy: 0.6367 - val_precision_8: 0.1356 - val_recall_8: 0.9444 - val_f1_score: 0.2065 Epoch 8/50 2678/2678 [==============================] - 37s 14ms/step - loss: 1.2930 - categorical_accuracy: 0.6183 - precision_8: 0.1358 - recall_8: 0.9613 - f1_score: 0.2466 - val_loss: 1.3676 - val_categorical_accuracy: 0.5957 - val_precision_8: 0.1354 - val_recall_8: 0.9425 - val_f1_score: 0.2057 Epoch 9/50 2678/2678 [==============================] - 37s 14ms/step - loss: 1.2968 - categorical_accuracy: 0.6173 - precision_8: 0.1373 - recall_8: 0.9599 - f1_score: 0.2601 - val_loss: 1.4133 - val_categorical_accuracy: 0.5779 - val_precision_8: 0.1341 - val_recall_8: 0.9377 - val_f1_score: 0.2073 Epoch 10/50 2678/2678 [==============================] - 37s 14ms/step - loss: 1.2909 - categorical_accuracy: 0.6170 - precision_8: 0.1379 - recall_8: 0.9613 - f1_score: 0.2594 - val_loss: 1.3471 - val_categorical_accuracy: 0.6150 - val_precision_8: 0.1371 - val_recall_8: 0.9385 - val_f1_score: 0.2018 Epoch 11/50 2678/2678 [==============================] - 40s 15ms/step - loss: 1.2856 - categorical_accuracy: 0.6215 - precision_8: 0.1403 - recall_8: 0.9603 - f1_score: 0.2675 - val_loss: 1.3609 - val_categorical_accuracy: 0.6345 - val_precision_8: 0.1401 - val_recall_8: 0.9442 - val_f1_score: 0.2157 Epoch 12/50 2678/2678 [==============================] - 39s 15ms/step - loss: 1.2865 - categorical_accuracy: 0.6189 - precision_8: 0.1390 - recall_8: 0.9613 - f1_score: 0.2713 - val_loss: 1.3613 - val_categorical_accuracy: 0.6000 - val_precision_8: 0.1417 - val_recall_8: 0.9320 - val_f1_score: 0.2214 Epoch 13/50 2678/2678 [==============================] - 37s 14ms/step - loss: 1.2840 - categorical_accuracy: 0.6287 - precision_8: 0.1411 - recall_8: 0.9519 - f1_score: 0.2638 - val_loss: 1.3594 - val_categorical_accuracy: 0.6226 - val_precision_8: 0.1387 - val_recall_8: 0.9323 - val_f1_score: 0.2085 Epoch 14/50 2678/2678 [==============================] - 39s 15ms/step - loss: 1.2796 - categorical_accuracy: 0.6241 - precision_8: 0.1405 - recall_8: 0.9524 - f1_score: 0.2741 - val_loss: 1.3435 - val_categorical_accuracy: 0.6356 - val_precision_8: 0.1415 - val_recall_8: 0.9317 - val_f1_score: 0.2194 Epoch 15/50 2678/2678 [==============================] - 38s 14ms/step - loss: 1.2792 - categorical_accuracy: 0.6251 - precision_8: 0.1428 - recall_8: 0.9533 - f1_score: 0.2816 - val_loss: 1.3648 - val_categorical_accuracy: 0.6264 - val_precision_8: 0.1411 - val_recall_8: 0.9301 - val_f1_score: 0.2145 4 32 15 0.001 190 47 Epoch 1/50 2679/2679 [==============================] - 39s 14ms/step - loss: 1.6523 - categorical_accuracy: 0.5177 - precision_9: 0.1230 - recall_9: 0.8855 - f1_score: 0.1589 - val_loss: 1.3559 - val_categorical_accuracy: 0.5835 - val_precision_9: 0.1261 - val_recall_9: 0.9467 - val_f1_score: 0.1672 Epoch 2/50 2679/2679 [==============================] - 38s 14ms/step - loss: 1.3897 - categorical_accuracy: 0.5780 - precision_9: 0.1292 - recall_9: 0.9470 - f1_score: 0.1887 - val_loss: 1.4041 - val_categorical_accuracy: 0.5973 - val_precision_9: 0.1297 - val_recall_9: 0.9551 - val_f1_score: 0.1792 Epoch 3/50 2679/2679 [==============================] - 38s 14ms/step - loss: 1.3788 - categorical_accuracy: 0.5882 - precision_9: 0.1289 - recall_9: 0.9531 - f1_score: 0.2030 - val_loss: 1.3189 - val_categorical_accuracy: 0.6165 - val_precision_9: 0.1304 - val_recall_9: 0.9494 - val_f1_score: 0.1762 Epoch 4/50 2679/2679 [==============================] - 38s 14ms/step - loss: 1.3455 - categorical_accuracy: 0.6002 - precision_9: 0.1305 - recall_9: 0.9538 - f1_score: 0.2175 - val_loss: 1.3091 - val_categorical_accuracy: 0.6245 - val_precision_9: 0.1280 - val_recall_9: 0.9459 - val_f1_score: 0.1813 Epoch 5/50 2679/2679 [==============================] - 39s 15ms/step - loss: 1.3368 - categorical_accuracy: 0.6000 - precision_9: 0.1316 - recall_9: 0.9545 - f1_score: 0.2241 - val_loss: 1.3298 - val_categorical_accuracy: 0.6189 - val_precision_9: 0.1347 - val_recall_9: 0.9419 - val_f1_score: 0.1821 Epoch 6/50 2679/2679 [==============================] - 38s 14ms/step - loss: 1.3366 - categorical_accuracy: 0.6065 - precision_9: 0.1330 - recall_9: 0.9506 - f1_score: 0.2287 - val_loss: 1.2623 - val_categorical_accuracy: 0.6596 - val_precision_9: 0.1313 - val_recall_9: 0.9540 - val_f1_score: 0.1845 Epoch 7/50 2679/2679 [==============================] - 38s 14ms/step - loss: 1.3161 - categorical_accuracy: 0.6190 - precision_9: 0.1325 - recall_9: 0.9519 - f1_score: 0.2391 - val_loss: 1.3004 - val_categorical_accuracy: 0.6308 - val_precision_9: 0.1359 - val_recall_9: 0.9515 - val_f1_score: 0.1928 Epoch 8/50 2679/2679 [==============================] - 39s 14ms/step - loss: 1.3123 - categorical_accuracy: 0.6140 - precision_9: 0.1349 - recall_9: 0.9531 - f1_score: 0.2408 - val_loss: 1.3326 - val_categorical_accuracy: 0.6348 - val_precision_9: 0.1346 - val_recall_9: 0.9488 - val_f1_score: 0.2008 Epoch 9/50 2679/2679 [==============================] - 38s 14ms/step - loss: 1.3177 - categorical_accuracy: 0.6115 - precision_9: 0.1346 - recall_9: 0.9505 - f1_score: 0.2495 - val_loss: 1.3136 - val_categorical_accuracy: 0.6320 - val_precision_9: 0.1380 - val_recall_9: 0.9475 - val_f1_score: 0.1936 Epoch 10/50 2679/2679 [==============================] - 38s 14ms/step - loss: 1.3051 - categorical_accuracy: 0.6152 - precision_9: 0.1372 - recall_9: 0.9512 - f1_score: 0.2584 - val_loss: 1.2731 - val_categorical_accuracy: 0.6247 - val_precision_9: 0.1376 - val_recall_9: 0.9524 - val_f1_score: 0.2089 Epoch 11/50 2679/2679 [==============================] - 38s 14ms/step - loss: 1.3026 - categorical_accuracy: 0.6143 - precision_9: 0.1372 - recall_9: 0.9459 - f1_score: 0.2607 - val_loss: 1.2847 - val_categorical_accuracy: 0.6625 - val_precision_9: 0.1389 - val_recall_9: 0.9447 - val_f1_score: 0.2080 Epoch 12/50 2679/2679 [==============================] - 38s 14ms/step - loss: 1.2917 - categorical_accuracy: 0.6248 - precision_9: 0.1380 - recall_9: 0.9444 - f1_score: 0.2482 - val_loss: 1.2855 - val_categorical_accuracy: 0.6687 - val_precision_9: 0.1403 - val_recall_9: 0.9480 - val_f1_score: 0.2107 Epoch 13/50 2679/2679 [==============================] - 38s 14ms/step - loss: 1.2995 - categorical_accuracy: 0.6208 - precision_9: 0.1402 - recall_9: 0.9451 - f1_score: 0.2697 - val_loss: 1.2269 - val_categorical_accuracy: 0.6753 - val_precision_9: 0.1381 - val_recall_9: 0.9464 - val_f1_score: 0.2043 Epoch 14/50 2679/2679 [==============================] - 38s 14ms/step - loss: 1.2921 - categorical_accuracy: 0.6227 - precision_9: 0.1419 - recall_9: 0.9458 - f1_score: 0.2767 - val_loss: 1.2962 - val_categorical_accuracy: 0.6529 - val_precision_9: 0.1431 - val_recall_9: 0.9460 - val_f1_score: 0.2028 Epoch 15/50 2679/2679 [==============================] - 38s 14ms/step - loss: 1.2925 - categorical_accuracy: 0.6263 - precision_9: 0.1416 - recall_9: 0.9429 - f1_score: 0.2728 - val_loss: 1.2496 - val_categorical_accuracy: 0.6617 - val_precision_9: 0.1426 - val_recall_9: 0.9313 - val_f1_score: 0.2070
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5. warnings.warn(
0 32 30 0.0001 189 48 Epoch 1/50 2584/2584 [==============================] - 72s 27ms/step - loss: 2.1609 - categorical_accuracy: 0.3926 - precision_10: 0.1043 - recall_10: 0.7741 - f1_score: 0.1456 - val_loss: 1.9377 - val_categorical_accuracy: 0.4388 - val_precision_10: 0.1139 - val_recall_10: 0.8315 - val_f1_score: 0.1411 Epoch 2/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.5966 - categorical_accuracy: 0.5307 - precision_10: 0.1270 - recall_10: 0.8803 - f1_score: 0.1594 - val_loss: 1.7016 - val_categorical_accuracy: 0.4890 - val_precision_10: 0.1276 - val_recall_10: 0.8881 - val_f1_score: 0.1533 Epoch 3/50 2584/2584 [==============================] - 43s 16ms/step - loss: 1.4295 - categorical_accuracy: 0.5676 - precision_10: 0.1335 - recall_10: 0.9166 - f1_score: 0.1682 - val_loss: 1.6125 - val_categorical_accuracy: 0.5074 - val_precision_10: 0.1290 - val_recall_10: 0.9097 - val_f1_score: 0.1595 Epoch 4/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.3568 - categorical_accuracy: 0.5899 - precision_10: 0.1331 - recall_10: 0.9338 - f1_score: 0.1749 - val_loss: 1.5847 - val_categorical_accuracy: 0.5190 - val_precision_10: 0.1305 - val_recall_10: 0.9202 - val_f1_score: 0.1691 Epoch 5/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.3173 - categorical_accuracy: 0.5911 - precision_10: 0.1334 - recall_10: 0.9477 - f1_score: 0.1817 - val_loss: 1.5556 - val_categorical_accuracy: 0.5290 - val_precision_10: 0.1293 - val_recall_10: 0.9240 - val_f1_score: 0.1692 Epoch 6/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.2902 - categorical_accuracy: 0.6060 - precision_10: 0.1330 - recall_10: 0.9510 - f1_score: 0.1882 - val_loss: 1.5486 - val_categorical_accuracy: 0.5268 - val_precision_10: 0.1300 - val_recall_10: 0.9294 - val_f1_score: 0.1764 Epoch 7/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.2708 - categorical_accuracy: 0.6081 - precision_10: 0.1340 - recall_10: 0.9567 - f1_score: 0.1898 - val_loss: 1.5340 - val_categorical_accuracy: 0.5377 - val_precision_10: 0.1289 - val_recall_10: 0.9301 - val_f1_score: 0.1827 Epoch 8/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.2560 - categorical_accuracy: 0.6107 - precision_10: 0.1335 - recall_10: 0.9598 - f1_score: 0.1917 - val_loss: 1.5375 - val_categorical_accuracy: 0.5349 - val_precision_10: 0.1281 - val_recall_10: 0.9341 - val_f1_score: 0.1859 Epoch 9/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.2446 - categorical_accuracy: 0.6178 - precision_10: 0.1334 - recall_10: 0.9600 - f1_score: 0.1926 - val_loss: 1.5376 - val_categorical_accuracy: 0.5446 - val_precision_10: 0.1292 - val_recall_10: 0.9380 - val_f1_score: 0.1907 Epoch 10/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.2315 - categorical_accuracy: 0.6250 - precision_10: 0.1321 - recall_10: 0.9623 - f1_score: 0.1947 - val_loss: 1.5454 - val_categorical_accuracy: 0.5467 - val_precision_10: 0.1291 - val_recall_10: 0.9397 - val_f1_score: 0.1896 Epoch 11/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.2254 - categorical_accuracy: 0.6202 - precision_10: 0.1325 - recall_10: 0.9643 - f1_score: 0.1958 - val_loss: 1.5498 - val_categorical_accuracy: 0.5483 - val_precision_10: 0.1285 - val_recall_10: 0.9387 - val_f1_score: 0.1903 Epoch 12/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.2198 - categorical_accuracy: 0.6263 - precision_10: 0.1325 - recall_10: 0.9638 - f1_score: 0.1973 - val_loss: 1.5508 - val_categorical_accuracy: 0.5458 - val_precision_10: 0.1274 - val_recall_10: 0.9399 - val_f1_score: 0.1903 1 32 30 0.0001 189 48 Epoch 1/50 2595/2595 [==============================] - 43s 16ms/step - loss: 2.1147 - categorical_accuracy: 0.4145 - precision_11: 0.1118 - recall_11: 0.7779 - f1_score: 0.1538 - val_loss: 1.7829 - val_categorical_accuracy: 0.5070 - val_precision_11: 0.1228 - val_recall_11: 0.8431 - val_f1_score: 0.1587 Epoch 2/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.5859 - categorical_accuracy: 0.5459 - precision_11: 0.1328 - recall_11: 0.8937 - f1_score: 0.1679 - val_loss: 1.5470 - val_categorical_accuracy: 0.5488 - val_precision_11: 0.1399 - val_recall_11: 0.8955 - val_f1_score: 0.1715 Epoch 3/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.4412 - categorical_accuracy: 0.5747 - precision_11: 0.1396 - recall_11: 0.9205 - f1_score: 0.1780 - val_loss: 1.4706 - val_categorical_accuracy: 0.5704 - val_precision_11: 0.1370 - val_recall_11: 0.9151 - val_f1_score: 0.1735 Epoch 4/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.3740 - categorical_accuracy: 0.5911 - precision_11: 0.1385 - recall_11: 0.9322 - f1_score: 0.1839 - val_loss: 1.4256 - val_categorical_accuracy: 0.5617 - val_precision_11: 0.1367 - val_recall_11: 0.9251 - val_f1_score: 0.1825 Epoch 5/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.3337 - categorical_accuracy: 0.5953 - precision_11: 0.1382 - recall_11: 0.9391 - f1_score: 0.1913 - val_loss: 1.3877 - val_categorical_accuracy: 0.5698 - val_precision_11: 0.1382 - val_recall_11: 0.9344 - val_f1_score: 0.1875 Epoch 6/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.3087 - categorical_accuracy: 0.5995 - precision_11: 0.1369 - recall_11: 0.9426 - f1_score: 0.1979 - val_loss: 1.3644 - val_categorical_accuracy: 0.5788 - val_precision_11: 0.1379 - val_recall_11: 0.9386 - val_f1_score: 0.1903 Epoch 7/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2890 - categorical_accuracy: 0.6025 - precision_11: 0.1370 - recall_11: 0.9448 - f1_score: 0.2082 - val_loss: 1.3620 - val_categorical_accuracy: 0.5711 - val_precision_11: 0.1366 - val_recall_11: 0.9461 - val_f1_score: 0.1918 Epoch 8/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2758 - categorical_accuracy: 0.6089 - precision_11: 0.1361 - recall_11: 0.9488 - f1_score: 0.2093 - val_loss: 1.3273 - val_categorical_accuracy: 0.5816 - val_precision_11: 0.1360 - val_recall_11: 0.9475 - val_f1_score: 0.1929 Epoch 9/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2635 - categorical_accuracy: 0.6105 - precision_11: 0.1354 - recall_11: 0.9500 - f1_score: 0.2154 - val_loss: 1.3259 - val_categorical_accuracy: 0.5858 - val_precision_11: 0.1363 - val_recall_11: 0.9510 - val_f1_score: 0.1953 Epoch 10/50 2595/2595 [==============================] - 42s 16ms/step - loss: 1.2527 - categorical_accuracy: 0.6169 - precision_11: 0.1355 - recall_11: 0.9511 - f1_score: 0.2210 - val_loss: 1.3275 - val_categorical_accuracy: 0.5934 - val_precision_11: 0.1352 - val_recall_11: 0.9501 - val_f1_score: 0.1948 Epoch 11/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2469 - categorical_accuracy: 0.6157 - precision_11: 0.1351 - recall_11: 0.9519 - f1_score: 0.2266 - val_loss: 1.3152 - val_categorical_accuracy: 0.5918 - val_precision_11: 0.1365 - val_recall_11: 0.9477 - val_f1_score: 0.1976 Epoch 12/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2399 - categorical_accuracy: 0.6205 - precision_11: 0.1354 - recall_11: 0.9530 - f1_score: 0.2318 - val_loss: 1.3173 - val_categorical_accuracy: 0.5863 - val_precision_11: 0.1364 - val_recall_11: 0.9531 - val_f1_score: 0.1977 Epoch 13/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2329 - categorical_accuracy: 0.6272 - precision_11: 0.1343 - recall_11: 0.9528 - f1_score: 0.2309 - val_loss: 1.3119 - val_categorical_accuracy: 0.5788 - val_precision_11: 0.1365 - val_recall_11: 0.9557 - val_f1_score: 0.2007 Epoch 14/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2282 - categorical_accuracy: 0.6210 - precision_11: 0.1341 - recall_11: 0.9547 - f1_score: 0.2361 - val_loss: 1.3061 - val_categorical_accuracy: 0.5908 - val_precision_11: 0.1360 - val_recall_11: 0.9581 - val_f1_score: 0.1978 Epoch 15/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2256 - categorical_accuracy: 0.6237 - precision_11: 0.1353 - recall_11: 0.9562 - f1_score: 0.2411 - val_loss: 1.3034 - val_categorical_accuracy: 0.5965 - val_precision_11: 0.1348 - val_recall_11: 0.9546 - val_f1_score: 0.1973 Epoch 16/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2173 - categorical_accuracy: 0.6312 - precision_11: 0.1345 - recall_11: 0.9553 - f1_score: 0.2425 - val_loss: 1.3094 - val_categorical_accuracy: 0.5970 - val_precision_11: 0.1350 - val_recall_11: 0.9555 - val_f1_score: 0.2012 Epoch 17/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2180 - categorical_accuracy: 0.6295 - precision_11: 0.1338 - recall_11: 0.9557 - f1_score: 0.2392 - val_loss: 1.2952 - val_categorical_accuracy: 0.6124 - val_precision_11: 0.1369 - val_recall_11: 0.9618 - val_f1_score: 0.2050 Epoch 18/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2146 - categorical_accuracy: 0.6279 - precision_11: 0.1341 - recall_11: 0.9556 - f1_score: 0.2489 - val_loss: 1.2954 - val_categorical_accuracy: 0.6013 - val_precision_11: 0.1371 - val_recall_11: 0.9631 - val_f1_score: 0.2036 Epoch 19/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2108 - categorical_accuracy: 0.6329 - precision_11: 0.1340 - recall_11: 0.9583 - f1_score: 0.2476 - val_loss: 1.3029 - val_categorical_accuracy: 0.6026 - val_precision_11: 0.1362 - val_recall_11: 0.9605 - val_f1_score: 0.2062 Epoch 20/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2114 - categorical_accuracy: 0.6330 - precision_11: 0.1342 - recall_11: 0.9559 - f1_score: 0.2505 - val_loss: 1.2968 - val_categorical_accuracy: 0.6000 - val_precision_11: 0.1343 - val_recall_11: 0.9582 - val_f1_score: 0.2014 Epoch 21/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2064 - categorical_accuracy: 0.6323 - precision_11: 0.1334 - recall_11: 0.9562 - f1_score: 0.2491 - val_loss: 1.2942 - val_categorical_accuracy: 0.6244 - val_precision_11: 0.1365 - val_recall_11: 0.9593 - val_f1_score: 0.2035 Epoch 22/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2062 - categorical_accuracy: 0.6324 - precision_11: 0.1343 - recall_11: 0.9573 - f1_score: 0.2556 - val_loss: 1.2832 - val_categorical_accuracy: 0.6149 - val_precision_11: 0.1377 - val_recall_11: 0.9616 - val_f1_score: 0.2101 Epoch 23/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2019 - categorical_accuracy: 0.6365 - precision_11: 0.1344 - recall_11: 0.9579 - f1_score: 0.2566 - val_loss: 1.2974 - val_categorical_accuracy: 0.6024 - val_precision_11: 0.1374 - val_recall_11: 0.9594 - val_f1_score: 0.2116 Epoch 24/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2013 - categorical_accuracy: 0.6354 - precision_11: 0.1339 - recall_11: 0.9573 - f1_score: 0.2592 - val_loss: 1.2866 - val_categorical_accuracy: 0.6296 - val_precision_11: 0.1350 - val_recall_11: 0.9584 - val_f1_score: 0.2032 Epoch 25/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.2011 - categorical_accuracy: 0.6382 - precision_11: 0.1336 - recall_11: 0.9578 - f1_score: 0.2569 - val_loss: 1.2804 - val_categorical_accuracy: 0.6163 - val_precision_11: 0.1353 - val_recall_11: 0.9584 - val_f1_score: 0.2031 Epoch 26/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.1988 - categorical_accuracy: 0.6409 - precision_11: 0.1344 - recall_11: 0.9584 - f1_score: 0.2556 - val_loss: 1.2815 - val_categorical_accuracy: 0.6188 - val_precision_11: 0.1356 - val_recall_11: 0.9635 - val_f1_score: 0.2041 2 32 30 0.0001 190 47 Epoch 1/50 2461/2461 [==============================] - 42s 17ms/step - loss: 2.1222 - categorical_accuracy: 0.4194 - precision_12: 0.0998 - recall_12: 0.7756 - f1_score: 0.1348 - val_loss: 2.0653 - val_categorical_accuracy: 0.4129 - val_precision_12: 0.1002 - val_recall_12: 0.7829 - val_f1_score: 0.1385 Epoch 2/50 2461/2461 [==============================] - 41s 16ms/step - loss: 1.5725 - categorical_accuracy: 0.5421 - precision_12: 0.1228 - recall_12: 0.8850 - f1_score: 0.1515 - val_loss: 2.0214 - val_categorical_accuracy: 0.4397 - val_precision_12: 0.1127 - val_recall_12: 0.8205 - val_f1_score: 0.1415 Epoch 3/50 2461/2461 [==============================] - 41s 17ms/step - loss: 1.4121 - categorical_accuracy: 0.5745 - precision_12: 0.1291 - recall_12: 0.9177 - f1_score: 0.1597 - val_loss: 2.0648 - val_categorical_accuracy: 0.4464 - val_precision_12: 0.1164 - val_recall_12: 0.8403 - val_f1_score: 0.1445 Epoch 4/50 2461/2461 [==============================] - 41s 17ms/step - loss: 1.3438 - categorical_accuracy: 0.5871 - precision_12: 0.1315 - recall_12: 0.9338 - f1_score: 0.1672 - val_loss: 2.0563 - val_categorical_accuracy: 0.4436 - val_precision_12: 0.1177 - val_recall_12: 0.8530 - val_f1_score: 0.1462 Epoch 5/50 2461/2461 [==============================] - 41s 16ms/step - loss: 1.3044 - categorical_accuracy: 0.5904 - precision_12: 0.1322 - recall_12: 0.9443 - f1_score: 0.1732 - val_loss: 2.0924 - val_categorical_accuracy: 0.4509 - val_precision_12: 0.1169 - val_recall_12: 0.8624 - val_f1_score: 0.1461 Epoch 6/50 2461/2461 [==============================] - 41s 17ms/step - loss: 1.2739 - categorical_accuracy: 0.6002 - precision_12: 0.1309 - recall_12: 0.9485 - f1_score: 0.1766 - val_loss: 2.1047 - val_categorical_accuracy: 0.4480 - val_precision_12: 0.1181 - val_recall_12: 0.8648 - val_f1_score: 0.1513 Epoch 7/50 2461/2461 [==============================] - 41s 17ms/step - loss: 1.2568 - categorical_accuracy: 0.6037 - precision_12: 0.1312 - recall_12: 0.9525 - f1_score: 0.1815 - val_loss: 2.1686 - val_categorical_accuracy: 0.4560 - val_precision_12: 0.1177 - val_recall_12: 0.8793 - val_f1_score: 0.1530 Epoch 8/50 2461/2461 [==============================] - 41s 17ms/step - loss: 1.2431 - categorical_accuracy: 0.6105 - precision_12: 0.1307 - recall_12: 0.9543 - f1_score: 0.1839 - val_loss: 2.1885 - val_categorical_accuracy: 0.4584 - val_precision_12: 0.1177 - val_recall_12: 0.8816 - val_f1_score: 0.1541 Epoch 9/50 2461/2461 [==============================] - 42s 17ms/step - loss: 1.2304 - categorical_accuracy: 0.6092 - precision_12: 0.1306 - recall_12: 0.9569 - f1_score: 0.1845 - val_loss: 2.2266 - val_categorical_accuracy: 0.4534 - val_precision_12: 0.1179 - val_recall_12: 0.8866 - val_f1_score: 0.1541 Epoch 10/50 2461/2461 [==============================] - 41s 16ms/step - loss: 1.2242 - categorical_accuracy: 0.6176 - precision_12: 0.1304 - recall_12: 0.9580 - f1_score: 0.1890 - val_loss: 2.2516 - val_categorical_accuracy: 0.4596 - val_precision_12: 0.1191 - val_recall_12: 0.8958 - val_f1_score: 0.1550 Epoch 11/50 2461/2461 [==============================] - 41s 17ms/step - loss: 1.2166 - categorical_accuracy: 0.6189 - precision_12: 0.1302 - recall_12: 0.9595 - f1_score: 0.1912 - val_loss: 2.2669 - val_categorical_accuracy: 0.4572 - val_precision_12: 0.1189 - val_recall_12: 0.9031 - val_f1_score: 0.1556 Epoch 12/50 2461/2461 [==============================] - 41s 17ms/step - loss: 1.2098 - categorical_accuracy: 0.6190 - precision_12: 0.1296 - recall_12: 0.9602 - f1_score: 0.1911 - val_loss: 2.2965 - val_categorical_accuracy: 0.4598 - val_precision_12: 0.1201 - val_recall_12: 0.9040 - val_f1_score: 0.1568 Epoch 13/50 2461/2461 [==============================] - 41s 17ms/step - loss: 1.2058 - categorical_accuracy: 0.6232 - precision_12: 0.1295 - recall_12: 0.9609 - f1_score: 0.1950 - val_loss: 2.3365 - val_categorical_accuracy: 0.4664 - val_precision_12: 0.1194 - val_recall_12: 0.8989 - val_f1_score: 0.1565 Epoch 14/50 2461/2461 [==============================] - 41s 17ms/step - loss: 1.2000 - categorical_accuracy: 0.6250 - precision_12: 0.1294 - recall_12: 0.9623 - f1_score: 0.1966 - val_loss: 2.3597 - val_categorical_accuracy: 0.4596 - val_precision_12: 0.1172 - val_recall_12: 0.8818 - val_f1_score: 0.1547 Epoch 15/50 2461/2461 [==============================] - 40s 16ms/step - loss: 1.1934 - categorical_accuracy: 0.6285 - precision_12: 0.1299 - recall_12: 0.9615 - f1_score: 0.2002 - val_loss: 2.3849 - val_categorical_accuracy: 0.4615 - val_precision_12: 0.1164 - val_recall_12: 0.8818 - val_f1_score: 0.1538 3 32 30 0.0001 190 47 Epoch 1/50 2627/2627 [==============================] - 43s 16ms/step - loss: 2.1257 - categorical_accuracy: 0.4160 - precision_13: 0.1072 - recall_13: 0.7821 - f1_score: 0.1490 - val_loss: 1.7528 - val_categorical_accuracy: 0.5236 - val_precision_13: 0.1207 - val_recall_13: 0.8439 - val_f1_score: 0.1521 Epoch 2/50 2627/2627 [==============================] - 41s 16ms/step - loss: 1.6143 - categorical_accuracy: 0.5359 - precision_13: 0.1272 - recall_13: 0.8772 - f1_score: 0.1609 - val_loss: 1.5304 - val_categorical_accuracy: 0.5660 - val_precision_13: 0.1341 - val_recall_13: 0.8963 - val_f1_score: 0.1615 Epoch 3/50 2627/2627 [==============================] - 41s 16ms/step - loss: 1.4544 - categorical_accuracy: 0.5697 - precision_13: 0.1343 - recall_13: 0.9146 - f1_score: 0.1712 - val_loss: 1.4320 - val_categorical_accuracy: 0.5830 - val_precision_13: 0.1336 - val_recall_13: 0.9181 - val_f1_score: 0.1697 Epoch 4/50 2627/2627 [==============================] - 41s 16ms/step - loss: 1.3813 - categorical_accuracy: 0.5784 - precision_13: 0.1342 - recall_13: 0.9360 - f1_score: 0.1801 - val_loss: 1.4120 - val_categorical_accuracy: 0.5847 - val_precision_13: 0.1326 - val_recall_13: 0.9250 - val_f1_score: 0.1730 Epoch 5/50 2627/2627 [==============================] - 41s 16ms/step - loss: 1.3369 - categorical_accuracy: 0.5916 - precision_13: 0.1340 - recall_13: 0.9432 - f1_score: 0.1890 - val_loss: 1.3982 - val_categorical_accuracy: 0.5816 - val_precision_13: 0.1321 - val_recall_13: 0.9321 - val_f1_score: 0.1809 Epoch 6/50 2627/2627 [==============================] - 41s 16ms/step - loss: 1.3114 - categorical_accuracy: 0.5960 - precision_13: 0.1341 - recall_13: 0.9523 - f1_score: 0.1983 - val_loss: 1.3728 - val_categorical_accuracy: 0.5826 - val_precision_13: 0.1317 - val_recall_13: 0.9335 - val_f1_score: 0.1834 Epoch 7/50 2627/2627 [==============================] - 41s 16ms/step - loss: 1.2892 - categorical_accuracy: 0.6033 - precision_13: 0.1340 - recall_13: 0.9561 - f1_score: 0.2062 - val_loss: 1.3574 - val_categorical_accuracy: 0.5975 - val_precision_13: 0.1316 - val_recall_13: 0.9356 - val_f1_score: 0.1844 Epoch 8/50 2627/2627 [==============================] - 41s 16ms/step - loss: 1.2745 - categorical_accuracy: 0.6054 - precision_13: 0.1344 - recall_13: 0.9591 - f1_score: 0.2104 - val_loss: 1.3413 - val_categorical_accuracy: 0.5987 - val_precision_13: 0.1315 - val_recall_13: 0.9379 - val_f1_score: 0.1887 Epoch 9/50 2627/2627 [==============================] - 42s 16ms/step - loss: 1.2618 - categorical_accuracy: 0.6083 - precision_13: 0.1333 - recall_13: 0.9623 - f1_score: 0.2186 - val_loss: 1.3354 - val_categorical_accuracy: 0.6062 - val_precision_13: 0.1327 - val_recall_13: 0.9381 - val_f1_score: 0.1914 Epoch 10/50 2627/2627 [==============================] - 43s 16ms/step - loss: 1.2476 - categorical_accuracy: 0.6172 - precision_13: 0.1342 - recall_13: 0.9635 - f1_score: 0.2181 - val_loss: 1.3316 - val_categorical_accuracy: 0.5989 - val_precision_13: 0.1329 - val_recall_13: 0.9403 - val_f1_score: 0.1923 Epoch 11/50 2627/2627 [==============================] - 41s 16ms/step - loss: 1.2437 - categorical_accuracy: 0.6155 - precision_13: 0.1348 - recall_13: 0.9660 - f1_score: 0.2239 - val_loss: 1.3380 - val_categorical_accuracy: 0.5907 - val_precision_13: 0.1329 - val_recall_13: 0.9429 - val_f1_score: 0.1975 Epoch 12/50 2627/2627 [==============================] - 41s 16ms/step - loss: 1.2348 - categorical_accuracy: 0.6175 - precision_13: 0.1337 - recall_13: 0.9679 - f1_score: 0.2251 - val_loss: 1.3131 - val_categorical_accuracy: 0.6111 - val_precision_13: 0.1322 - val_recall_13: 0.9426 - val_f1_score: 0.1931 Epoch 13/50 2627/2627 [==============================] - 41s 16ms/step - loss: 1.2309 - categorical_accuracy: 0.6201 - precision_13: 0.1339 - recall_13: 0.9685 - f1_score: 0.2293 - val_loss: 1.3360 - val_categorical_accuracy: 0.5978 - val_precision_13: 0.1322 - val_recall_13: 0.9421 - val_f1_score: 0.1960 Epoch 14/50 2627/2627 [==============================] - 41s 16ms/step - loss: 1.2267 - categorical_accuracy: 0.6227 - precision_13: 0.1342 - recall_13: 0.9692 - f1_score: 0.2340 - val_loss: 1.3160 - val_categorical_accuracy: 0.6037 - val_precision_13: 0.1319 - val_recall_13: 0.9426 - val_f1_score: 0.1947 4 32 30 0.0001 190 47 Epoch 1/50 2625/2625 [==============================] - 42s 16ms/step - loss: 2.1486 - categorical_accuracy: 0.4036 - precision_14: 0.1061 - recall_14: 0.7775 - f1_score: 0.1485 - val_loss: 1.6783 - val_categorical_accuracy: 0.5306 - val_precision_14: 0.1234 - val_recall_14: 0.8706 - val_f1_score: 0.1536 Epoch 2/50 2625/2625 [==============================] - 40s 15ms/step - loss: 1.6196 - categorical_accuracy: 0.5343 - precision_14: 0.1270 - recall_14: 0.8828 - f1_score: 0.1626 - val_loss: 1.4649 - val_categorical_accuracy: 0.5741 - val_precision_14: 0.1373 - val_recall_14: 0.9140 - val_f1_score: 0.1613 Epoch 3/50 2625/2625 [==============================] - 41s 16ms/step - loss: 1.4577 - categorical_accuracy: 0.5630 - precision_14: 0.1355 - recall_14: 0.9192 - f1_score: 0.1732 - val_loss: 1.3848 - val_categorical_accuracy: 0.5892 - val_precision_14: 0.1368 - val_recall_14: 0.9295 - val_f1_score: 0.1684 Epoch 4/50 2625/2625 [==============================] - 40s 15ms/step - loss: 1.3877 - categorical_accuracy: 0.5770 - precision_14: 0.1352 - recall_14: 0.9335 - f1_score: 0.1836 - val_loss: 1.3539 - val_categorical_accuracy: 0.5852 - val_precision_14: 0.1367 - val_recall_14: 0.9382 - val_f1_score: 0.1757 Epoch 5/50 2625/2625 [==============================] - 41s 15ms/step - loss: 1.3467 - categorical_accuracy: 0.5895 - precision_14: 0.1342 - recall_14: 0.9376 - f1_score: 0.1865 - val_loss: 1.3210 - val_categorical_accuracy: 0.6000 - val_precision_14: 0.1356 - val_recall_14: 0.9439 - val_f1_score: 0.1799 Epoch 6/50 2625/2625 [==============================] - 40s 15ms/step - loss: 1.3201 - categorical_accuracy: 0.5900 - precision_14: 0.1332 - recall_14: 0.9444 - f1_score: 0.1904 - val_loss: 1.3066 - val_categorical_accuracy: 0.5991 - val_precision_14: 0.1358 - val_recall_14: 0.9472 - val_f1_score: 0.1790 Epoch 7/50 2625/2625 [==============================] - 40s 15ms/step - loss: 1.3021 - categorical_accuracy: 0.5912 - precision_14: 0.1340 - recall_14: 0.9506 - f1_score: 0.2058 - val_loss: 1.2839 - val_categorical_accuracy: 0.6197 - val_precision_14: 0.1334 - val_recall_14: 0.9478 - val_f1_score: 0.1787 Epoch 8/50 2625/2625 [==============================] - 41s 15ms/step - loss: 1.2864 - categorical_accuracy: 0.5987 - precision_14: 0.1336 - recall_14: 0.9528 - f1_score: 0.2085 - val_loss: 1.2799 - val_categorical_accuracy: 0.6105 - val_precision_14: 0.1352 - val_recall_14: 0.9512 - val_f1_score: 0.1846 Epoch 9/50 2625/2625 [==============================] - 40s 15ms/step - loss: 1.2754 - categorical_accuracy: 0.6012 - precision_14: 0.1329 - recall_14: 0.9523 - f1_score: 0.2058 - val_loss: 1.2717 - val_categorical_accuracy: 0.6185 - val_precision_14: 0.1339 - val_recall_14: 0.9539 - val_f1_score: 0.1826 Epoch 10/50 2625/2625 [==============================] - 40s 15ms/step - loss: 1.2667 - categorical_accuracy: 0.6091 - precision_14: 0.1320 - recall_14: 0.9553 - f1_score: 0.2141 - val_loss: 1.2497 - val_categorical_accuracy: 0.6152 - val_precision_14: 0.1338 - val_recall_14: 0.9543 - val_f1_score: 0.1809 Epoch 11/50 2625/2625 [==============================] - 40s 15ms/step - loss: 1.2572 - categorical_accuracy: 0.6083 - precision_14: 0.1324 - recall_14: 0.9567 - f1_score: 0.2109 - val_loss: 1.2476 - val_categorical_accuracy: 0.6246 - val_precision_14: 0.1318 - val_recall_14: 0.9539 - val_f1_score: 0.1802 0 32 30 0.001 189 48 Epoch 1/50
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5. warnings.warn(
2584/2584 [==============================] - 42s 16ms/step - loss: 1.6169 - categorical_accuracy: 0.5406 - precision_15: 0.1329 - recall_15: 0.8979 - f1_score: 0.1701 - val_loss: 1.5946 - val_categorical_accuracy: 0.5549 - val_precision_15: 0.1326 - val_recall_15: 0.9290 - val_f1_score: 0.1893 Epoch 2/50 2584/2584 [==============================] - 40s 16ms/step - loss: 1.3807 - categorical_accuracy: 0.5882 - precision_15: 0.1363 - recall_15: 0.9530 - f1_score: 0.1983 - val_loss: 1.5753 - val_categorical_accuracy: 0.5482 - val_precision_15: 0.1321 - val_recall_15: 0.9336 - val_f1_score: 0.1880 Epoch 3/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.3191 - categorical_accuracy: 0.6049 - precision_15: 0.1340 - recall_15: 0.9572 - f1_score: 0.2008 - val_loss: 1.5953 - val_categorical_accuracy: 0.5349 - val_precision_15: 0.1293 - val_recall_15: 0.9399 - val_f1_score: 0.1904 Epoch 4/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.3012 - categorical_accuracy: 0.6148 - precision_15: 0.1332 - recall_15: 0.9573 - f1_score: 0.2066 - val_loss: 1.5855 - val_categorical_accuracy: 0.5036 - val_precision_15: 0.1321 - val_recall_15: 0.9577 - val_f1_score: 0.1989 Epoch 5/50 2584/2584 [==============================] - 40s 16ms/step - loss: 1.2977 - categorical_accuracy: 0.6136 - precision_15: 0.1342 - recall_15: 0.9624 - f1_score: 0.2146 - val_loss: 1.6147 - val_categorical_accuracy: 0.5392 - val_precision_15: 0.1281 - val_recall_15: 0.9359 - val_f1_score: 0.1916 Epoch 6/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.2669 - categorical_accuracy: 0.6172 - precision_15: 0.1351 - recall_15: 0.9598 - f1_score: 0.2190 - val_loss: 1.5585 - val_categorical_accuracy: 0.5612 - val_precision_15: 0.1325 - val_recall_15: 0.9426 - val_f1_score: 0.2068 Epoch 7/50 2584/2584 [==============================] - 40s 16ms/step - loss: 1.2566 - categorical_accuracy: 0.6227 - precision_15: 0.1358 - recall_15: 0.9587 - f1_score: 0.2204 - val_loss: 1.5598 - val_categorical_accuracy: 0.5693 - val_precision_15: 0.1284 - val_recall_15: 0.9423 - val_f1_score: 0.2004 Epoch 8/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.2395 - categorical_accuracy: 0.6335 - precision_15: 0.1365 - recall_15: 0.9591 - f1_score: 0.2235 - val_loss: 1.6191 - val_categorical_accuracy: 0.5542 - val_precision_15: 0.1312 - val_recall_15: 0.9438 - val_f1_score: 0.2070 Epoch 9/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.2426 - categorical_accuracy: 0.6278 - precision_15: 0.1370 - recall_15: 0.9567 - f1_score: 0.2283 - val_loss: 1.5468 - val_categorical_accuracy: 0.5630 - val_precision_15: 0.1290 - val_recall_15: 0.9468 - val_f1_score: 0.1986 Epoch 10/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.2338 - categorical_accuracy: 0.6284 - precision_15: 0.1387 - recall_15: 0.9576 - f1_score: 0.2309 - val_loss: 1.5787 - val_categorical_accuracy: 0.5531 - val_precision_15: 0.1337 - val_recall_15: 0.9393 - val_f1_score: 0.2103 Epoch 11/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.2236 - categorical_accuracy: 0.6353 - precision_15: 0.1400 - recall_15: 0.9564 - f1_score: 0.2357 - val_loss: 1.5304 - val_categorical_accuracy: 0.5546 - val_precision_15: 0.1327 - val_recall_15: 0.9371 - val_f1_score: 0.2056 Epoch 12/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.2183 - categorical_accuracy: 0.6359 - precision_15: 0.1394 - recall_15: 0.9522 - f1_score: 0.2353 - val_loss: 1.5987 - val_categorical_accuracy: 0.5835 - val_precision_15: 0.1344 - val_recall_15: 0.9279 - val_f1_score: 0.2111 Epoch 13/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.2113 - categorical_accuracy: 0.6359 - precision_15: 0.1403 - recall_15: 0.9533 - f1_score: 0.2399 - val_loss: 1.5350 - val_categorical_accuracy: 0.5702 - val_precision_15: 0.1359 - val_recall_15: 0.9258 - val_f1_score: 0.2121 Epoch 14/50 2584/2584 [==============================] - 40s 16ms/step - loss: 1.1995 - categorical_accuracy: 0.6491 - precision_15: 0.1431 - recall_15: 0.9493 - f1_score: 0.2422 - val_loss: 1.5781 - val_categorical_accuracy: 0.5537 - val_precision_15: 0.1358 - val_recall_15: 0.9353 - val_f1_score: 0.2119 Epoch 15/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.1991 - categorical_accuracy: 0.6415 - precision_15: 0.1450 - recall_15: 0.9532 - f1_score: 0.2479 - val_loss: 1.5787 - val_categorical_accuracy: 0.5260 - val_precision_15: 0.1382 - val_recall_15: 0.9332 - val_f1_score: 0.2223 Epoch 16/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.1988 - categorical_accuracy: 0.6390 - precision_15: 0.1431 - recall_15: 0.9460 - f1_score: 0.2488 - val_loss: 1.5611 - val_categorical_accuracy: 0.5885 - val_precision_15: 0.1444 - val_recall_15: 0.9237 - val_f1_score: 0.2219 Epoch 17/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.1979 - categorical_accuracy: 0.6455 - precision_15: 0.1461 - recall_15: 0.9461 - f1_score: 0.2505 - val_loss: 1.5341 - val_categorical_accuracy: 0.5576 - val_precision_15: 0.1422 - val_recall_15: 0.9234 - val_f1_score: 0.2159 Epoch 18/50 2584/2584 [==============================] - 41s 16ms/step - loss: 1.1976 - categorical_accuracy: 0.6458 - precision_15: 0.1463 - recall_15: 0.9480 - f1_score: 0.2511 - val_loss: 1.5409 - val_categorical_accuracy: 0.5722 - val_precision_15: 0.1391 - val_recall_15: 0.9310 - val_f1_score: 0.2205 1 32 30 0.001 189 48 Epoch 1/50 2595/2595 [==============================] - 42s 16ms/step - loss: 1.6351 - categorical_accuracy: 0.5271 - precision_16: 0.1210 - recall_16: 0.9008 - f1_score: 0.1575 - val_loss: 1.4816 - val_categorical_accuracy: 0.5737 - val_precision_16: 0.1316 - val_recall_16: 0.9472 - val_f1_score: 0.1846 Epoch 2/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.4010 - categorical_accuracy: 0.5789 - precision_16: 0.1276 - recall_16: 0.9448 - f1_score: 0.1830 - val_loss: 1.4672 - val_categorical_accuracy: 0.5934 - val_precision_16: 0.1252 - val_recall_16: 0.9397 - val_f1_score: 0.1815 Epoch 3/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.3500 - categorical_accuracy: 0.5874 - precision_16: 0.1269 - recall_16: 0.9528 - f1_score: 0.1923 - val_loss: 1.3682 - val_categorical_accuracy: 0.6299 - val_precision_16: 0.1284 - val_recall_16: 0.9478 - val_f1_score: 0.1993 Epoch 4/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.3316 - categorical_accuracy: 0.5998 - precision_16: 0.1285 - recall_16: 0.9542 - f1_score: 0.2019 - val_loss: 1.3497 - val_categorical_accuracy: 0.5845 - val_precision_16: 0.1258 - val_recall_16: 0.9536 - val_f1_score: 0.1904 Epoch 5/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.3054 - categorical_accuracy: 0.6099 - precision_16: 0.1285 - recall_16: 0.9534 - f1_score: 0.2137 - val_loss: 1.3163 - val_categorical_accuracy: 0.6035 - val_precision_16: 0.1308 - val_recall_16: 0.9574 - val_f1_score: 0.1981 Epoch 6/50 2595/2595 [==============================] - 41s 16ms/step - loss: 1.3063 - categorical_accuracy: 0.6032 - precision_16: 0.1295 - recall_16: 0.9528 - f1_score: 0.2129 - val_loss: 1.3809 - val_categorical_accuracy: 0.5670 - val_precision_16: 0.1296 - val_recall_16: 0.9532 - val_f1_score: 0.1989 2 32 30 0.001 190 47 Epoch 1/50 2461/2461 [==============================] - 43s 17ms/step - loss: 1.5794 - categorical_accuracy: 0.5463 - precision_17: 0.1325 - recall_17: 0.8958 - f1_score: 0.1613 - val_loss: 2.1933 - val_categorical_accuracy: 0.4792 - val_precision_17: 0.1237 - val_recall_17: 0.8634 - val_f1_score: 0.1544 Epoch 2/50 2461/2461 [==============================] - 42s 17ms/step - loss: 1.3716 - categorical_accuracy: 0.5802 - precision_17: 0.1366 - recall_17: 0.9473 - f1_score: 0.1913 - val_loss: 2.1729 - val_categorical_accuracy: 0.4144 - val_precision_17: 0.1228 - val_recall_17: 0.8954 - val_f1_score: 0.1630 Epoch 3/50 2461/2461 [==============================] - 42s 17ms/step - loss: 1.3306 - categorical_accuracy: 0.5926 - precision_17: 0.1343 - recall_17: 0.9545 - f1_score: 0.2011 - val_loss: 2.2222 - val_categorical_accuracy: 0.4614 - val_precision_17: 0.1213 - val_recall_17: 0.8834 - val_f1_score: 0.1606 Epoch 4/50 2461/2461 [==============================] - 42s 17ms/step - loss: 1.2917 - categorical_accuracy: 0.6034 - precision_17: 0.1344 - recall_17: 0.9544 - f1_score: 0.2001 - val_loss: 2.2971 - val_categorical_accuracy: 0.4337 - val_precision_17: 0.1209 - val_recall_17: 0.8583 - val_f1_score: 0.1629 Epoch 5/50 2461/2461 [==============================] - 42s 17ms/step - loss: 1.2801 - categorical_accuracy: 0.6029 - precision_17: 0.1347 - recall_17: 0.9530 - f1_score: 0.2079 - val_loss: 2.2046 - val_categorical_accuracy: 0.4346 - val_precision_17: 0.1217 - val_recall_17: 0.8913 - val_f1_score: 0.1608 3 32 30 0.001 190 47 Epoch 1/50 2627/2627 [==============================] - 38s 14ms/step - loss: 1.6274 - categorical_accuracy: 0.5281 - precision_18: 0.1296 - recall_18: 0.9108 - f1_score: 0.1693 - val_loss: 1.4805 - val_categorical_accuracy: 0.5621 - val_precision_18: 0.1321 - val_recall_18: 0.9371 - val_f1_score: 0.1800 Epoch 2/50 2627/2627 [==============================] - 44s 17ms/step - loss: 1.4047 - categorical_accuracy: 0.5666 - precision_18: 0.1321 - recall_18: 0.9595 - f1_score: 0.1974 - val_loss: 1.4197 - val_categorical_accuracy: 0.5590 - val_precision_18: 0.1291 - val_recall_18: 0.9588 - val_f1_score: 0.1855 Epoch 3/50 2627/2627 [==============================] - 44s 17ms/step - loss: 1.3468 - categorical_accuracy: 0.5838 - precision_18: 0.1313 - recall_18: 0.9640 - f1_score: 0.2061 - val_loss: 1.4461 - val_categorical_accuracy: 0.5729 - val_precision_18: 0.1362 - val_recall_18: 0.9557 - val_f1_score: 0.1912 Epoch 4/50 2627/2627 [==============================] - 44s 17ms/step - loss: 1.3353 - categorical_accuracy: 0.5945 - precision_18: 0.1316 - recall_18: 0.9677 - f1_score: 0.2175 - val_loss: 1.4165 - val_categorical_accuracy: 0.5526 - val_precision_18: 0.1307 - val_recall_18: 0.9478 - val_f1_score: 0.1961 Epoch 5/50 2627/2627 [==============================] - 44s 17ms/step - loss: 1.3007 - categorical_accuracy: 0.5987 - precision_18: 0.1329 - recall_18: 0.9668 - f1_score: 0.2182 - val_loss: 1.3040 - val_categorical_accuracy: 0.6136 - val_precision_18: 0.1311 - val_recall_18: 0.9472 - val_f1_score: 0.1923 Epoch 6/50 2627/2627 [==============================] - 44s 17ms/step - loss: 1.2928 - categorical_accuracy: 0.6014 - precision_18: 0.1331 - recall_18: 0.9669 - f1_score: 0.2260 - val_loss: 1.3934 - val_categorical_accuracy: 0.5738 - val_precision_18: 0.1363 - val_recall_18: 0.9491 - val_f1_score: 0.2065 Epoch 7/50 2627/2627 [==============================] - 45s 17ms/step - loss: 1.2819 - categorical_accuracy: 0.6038 - precision_18: 0.1342 - recall_18: 0.9647 - f1_score: 0.2314 - val_loss: 1.3604 - val_categorical_accuracy: 0.5892 - val_precision_18: 0.1353 - val_recall_18: 0.9542 - val_f1_score: 0.2094 Epoch 8/50 2627/2627 [==============================] - 45s 17ms/step - loss: 1.2631 - categorical_accuracy: 0.6077 - precision_18: 0.1354 - recall_18: 0.9650 - f1_score: 0.2292 - val_loss: 1.3642 - val_categorical_accuracy: 0.5873 - val_precision_18: 0.1355 - val_recall_18: 0.9401 - val_f1_score: 0.2095 Epoch 9/50 2627/2627 [==============================] - 44s 17ms/step - loss: 1.2567 - categorical_accuracy: 0.6159 - precision_18: 0.1377 - recall_18: 0.9649 - f1_score: 0.2442 - val_loss: 1.2565 - val_categorical_accuracy: 0.6433 - val_precision_18: 0.1351 - val_recall_18: 0.9396 - val_f1_score: 0.2004 Epoch 10/50 2627/2627 [==============================] - 45s 17ms/step - loss: 1.2505 - categorical_accuracy: 0.6200 - precision_18: 0.1388 - recall_18: 0.9613 - f1_score: 0.2507 - val_loss: 1.3038 - val_categorical_accuracy: 0.6205 - val_precision_18: 0.1390 - val_recall_18: 0.9478 - val_f1_score: 0.2178 Epoch 11/50 2627/2627 [==============================] - 44s 17ms/step - loss: 1.2401 - categorical_accuracy: 0.6214 - precision_18: 0.1409 - recall_18: 0.9629 - f1_score: 0.2576 - val_loss: 1.2882 - val_categorical_accuracy: 0.6416 - val_precision_18: 0.1372 - val_recall_18: 0.9422 - val_f1_score: 0.2150 Epoch 12/50 2627/2627 [==============================] - 45s 17ms/step - loss: 1.2365 - categorical_accuracy: 0.6223 - precision_18: 0.1412 - recall_18: 0.9613 - f1_score: 0.2533 - val_loss: 1.3872 - val_categorical_accuracy: 0.5587 - val_precision_18: 0.1415 - val_recall_18: 0.9449 - val_f1_score: 0.2233 Epoch 13/50 2627/2627 [==============================] - 45s 17ms/step - loss: 1.2347 - categorical_accuracy: 0.6236 - precision_18: 0.1416 - recall_18: 0.9594 - f1_score: 0.2712 - val_loss: 1.2800 - val_categorical_accuracy: 0.6224 - val_precision_18: 0.1427 - val_recall_18: 0.9446 - val_f1_score: 0.2213 Epoch 14/50 2627/2627 [==============================] - 44s 17ms/step - loss: 1.2331 - categorical_accuracy: 0.6216 - precision_18: 0.1432 - recall_18: 0.9604 - f1_score: 0.2832 - val_loss: 1.3322 - val_categorical_accuracy: 0.5996 - val_precision_18: 0.1431 - val_recall_18: 0.9405 - val_f1_score: 0.2292 Epoch 15/50 2627/2627 [==============================] - 45s 17ms/step - loss: 1.2302 - categorical_accuracy: 0.6213 - precision_18: 0.1440 - recall_18: 0.9572 - f1_score: 0.2818 - val_loss: 1.2948 - val_categorical_accuracy: 0.6487 - val_precision_18: 0.1477 - val_recall_18: 0.9372 - val_f1_score: 0.2353 Epoch 16/50 2627/2627 [==============================] - 44s 17ms/step - loss: 1.2228 - categorical_accuracy: 0.6290 - precision_18: 0.1456 - recall_18: 0.9544 - f1_score: 0.2854 - val_loss: 1.2860 - val_categorical_accuracy: 0.6305 - val_precision_18: 0.1469 - val_recall_18: 0.9349 - val_f1_score: 0.2230 Epoch 17/50 2627/2627 [==============================] - 45s 17ms/step - loss: 1.2221 - categorical_accuracy: 0.6295 - precision_18: 0.1471 - recall_18: 0.9534 - f1_score: 0.2852 - val_loss: 1.3019 - val_categorical_accuracy: 0.6162 - val_precision_18: 0.1490 - val_recall_18: 0.9297 - val_f1_score: 0.2367 Epoch 18/50 2627/2627 [==============================] - 44s 17ms/step - loss: 1.2179 - categorical_accuracy: 0.6291 - precision_18: 0.1471 - recall_18: 0.9521 - f1_score: 0.2884 - val_loss: 1.3036 - val_categorical_accuracy: 0.6279 - val_precision_18: 0.1452 - val_recall_18: 0.9327 - val_f1_score: 0.2277 Epoch 19/50 2627/2627 [==============================] - 45s 17ms/step - loss: 1.2185 - categorical_accuracy: 0.6250 - precision_18: 0.1493 - recall_18: 0.9525 - f1_score: 0.2914 - val_loss: 1.3401 - val_categorical_accuracy: 0.5849 - val_precision_18: 0.1456 - val_recall_18: 0.9292 - val_f1_score: 0.2383 Epoch 20/50 2627/2627 [==============================] - 45s 17ms/step - loss: 1.2179 - categorical_accuracy: 0.6355 - precision_18: 0.1492 - recall_18: 0.9494 - f1_score: 0.2920 - val_loss: 1.2992 - val_categorical_accuracy: 0.5986 - val_precision_18: 0.1476 - val_recall_18: 0.9308 - val_f1_score: 0.2273 Epoch 21/50 2627/2627 [==============================] - 45s 17ms/step - loss: 1.2101 - categorical_accuracy: 0.6335 - precision_18: 0.1498 - recall_18: 0.9509 - f1_score: 0.2979 - val_loss: 1.2749 - val_categorical_accuracy: 0.6221 - val_precision_18: 0.1501 - val_recall_18: 0.9290 - val_f1_score: 0.2334 Epoch 22/50 2627/2627 [==============================] - 45s 17ms/step - loss: 1.2166 - categorical_accuracy: 0.6374 - precision_18: 0.1505 - recall_18: 0.9455 - f1_score: 0.2968 - val_loss: 1.2935 - val_categorical_accuracy: 0.6219 - val_precision_18: 0.1502 - val_recall_18: 0.9282 - val_f1_score: 0.2415 Epoch 23/50 2627/2627 [==============================] - 45s 17ms/step - loss: 1.2167 - categorical_accuracy: 0.6282 - precision_18: 0.1519 - recall_18: 0.9497 - f1_score: 0.3042 - val_loss: 1.2691 - val_categorical_accuracy: 0.6173 - val_precision_18: 0.1478 - val_recall_18: 0.9286 - val_f1_score: 0.2315 Epoch 24/50 2627/2627 [==============================] - 45s 17ms/step - loss: 1.2109 - categorical_accuracy: 0.6325 - precision_18: 0.1527 - recall_18: 0.9474 - f1_score: 0.3028 - val_loss: 1.2468 - val_categorical_accuracy: 0.6680 - val_precision_18: 0.1524 - val_recall_18: 0.9237 - val_f1_score: 0.2448 Epoch 25/50 2627/2627 [==============================] - 45s 17ms/step - loss: 1.2109 - categorical_accuracy: 0.6355 - precision_18: 0.1541 - recall_18: 0.9462 - f1_score: 0.3040 - val_loss: 1.2852 - val_categorical_accuracy: 0.6054 - val_precision_18: 0.1510 - val_recall_18: 0.9265 - val_f1_score: 0.2420 Epoch 26/50 2627/2627 [==============================] - 47s 18ms/step - loss: 1.2068 - categorical_accuracy: 0.6319 - precision_18: 0.1546 - recall_18: 0.9466 - f1_score: 0.3088 - val_loss: 1.3771 - val_categorical_accuracy: 0.5661 - val_precision_18: 0.1536 - val_recall_18: 0.9296 - val_f1_score: 0.2535 Epoch 27/50 2627/2627 [==============================] - 46s 18ms/step - loss: 1.2074 - categorical_accuracy: 0.6348 - precision_18: 0.1558 - recall_18: 0.9411 - f1_score: 0.3077 - val_loss: 1.2724 - val_categorical_accuracy: 0.6410 - val_precision_18: 0.1489 - val_recall_18: 0.9140 - val_f1_score: 0.2351 Epoch 28/50 2627/2627 [==============================] - 45s 17ms/step - loss: 1.2041 - categorical_accuracy: 0.6333 - precision_18: 0.1549 - recall_18: 0.9438 - f1_score: 0.3100 - val_loss: 1.2908 - val_categorical_accuracy: 0.6149 - val_precision_18: 0.1533 - val_recall_18: 0.9117 - val_f1_score: 0.2297 Epoch 29/50 2627/2627 [==============================] - 38s 14ms/step - loss: 1.2078 - categorical_accuracy: 0.6387 - precision_18: 0.1572 - recall_18: 0.9392 - f1_score: 0.3087 - val_loss: 1.3062 - val_categorical_accuracy: 0.6126 - val_precision_18: 0.1587 - val_recall_18: 0.9262 - val_f1_score: 0.2533 4 32 30 0.001 190 47 Epoch 1/50 2625/2625 [==============================] - 47s 18ms/step - loss: 1.6425 - categorical_accuracy: 0.5241 - precision_19: 0.1205 - recall_19: 0.9014 - f1_score: 0.1576 - val_loss: 1.3396 - val_categorical_accuracy: 0.5975 - val_precision_19: 0.1318 - val_recall_19: 0.9421 - val_f1_score: 0.1671 Epoch 2/50 2625/2625 [==============================] - 46s 17ms/step - loss: 1.4083 - categorical_accuracy: 0.5709 - precision_19: 0.1255 - recall_19: 0.9493 - f1_score: 0.1824 - val_loss: 1.3520 - val_categorical_accuracy: 0.6126 - val_precision_19: 0.1279 - val_recall_19: 0.9548 - val_f1_score: 0.1743 Epoch 3/50 2625/2625 [==============================] - 47s 18ms/step - loss: 1.3606 - categorical_accuracy: 0.5813 - precision_19: 0.1269 - recall_19: 0.9560 - f1_score: 0.1940 - val_loss: 1.2969 - val_categorical_accuracy: 0.6207 - val_precision_19: 0.1299 - val_recall_19: 0.9483 - val_f1_score: 0.1823 Epoch 4/50 2625/2625 [==============================] - 46s 18ms/step - loss: 1.3428 - categorical_accuracy: 0.5972 - precision_19: 0.1278 - recall_19: 0.9561 - f1_score: 0.2066 - val_loss: 1.2998 - val_categorical_accuracy: 0.6274 - val_precision_19: 0.1306 - val_recall_19: 0.9674 - val_f1_score: 0.1822 Epoch 5/50 2625/2625 [==============================] - 46s 17ms/step - loss: 1.3272 - categorical_accuracy: 0.5935 - precision_19: 0.1284 - recall_19: 0.9560 - f1_score: 0.2023 - val_loss: 1.3752 - val_categorical_accuracy: 0.6191 - val_precision_19: 0.1242 - val_recall_19: 0.9552 - val_f1_score: 0.1747 Epoch 6/50 2625/2625 [==============================] - 46s 18ms/step - loss: 1.3058 - categorical_accuracy: 0.6013 - precision_19: 0.1288 - recall_19: 0.9567 - f1_score: 0.2094 - val_loss: 1.2324 - val_categorical_accuracy: 0.6409 - val_precision_19: 0.1298 - val_recall_19: 0.9687 - val_f1_score: 0.1796
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5. warnings.warn(
0 64 15 0.0001 189 48 Epoch 1/50 1367/1367 [==============================] - 46s 33ms/step - loss: 2.5924 - categorical_accuracy: 0.2929 - precision_20: 0.0877 - recall_20: 0.6630 - f1_score: 0.1285 - val_loss: 2.3139 - val_categorical_accuracy: 0.3560 - val_precision_20: 0.0988 - val_recall_20: 0.7370 - val_f1_score: 0.1348 Epoch 2/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.9849 - categorical_accuracy: 0.4160 - precision_20: 0.1113 - recall_20: 0.8079 - f1_score: 0.1506 - val_loss: 2.0233 - val_categorical_accuracy: 0.4336 - val_precision_20: 0.1110 - val_recall_20: 0.8030 - val_f1_score: 0.1411 Epoch 3/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.7452 - categorical_accuracy: 0.4860 - precision_20: 0.1226 - recall_20: 0.8550 - f1_score: 0.1580 - val_loss: 1.8553 - val_categorical_accuracy: 0.4747 - val_precision_20: 0.1212 - val_recall_20: 0.8446 - val_f1_score: 0.1474 Epoch 4/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.6037 - categorical_accuracy: 0.5282 - precision_20: 0.1312 - recall_20: 0.8829 - f1_score: 0.1639 - val_loss: 1.7548 - val_categorical_accuracy: 0.4993 - val_precision_20: 0.1265 - val_recall_20: 0.8590 - val_f1_score: 0.1489 Epoch 5/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.5177 - categorical_accuracy: 0.5495 - precision_20: 0.1357 - recall_20: 0.8960 - f1_score: 0.1667 - val_loss: 1.6997 - val_categorical_accuracy: 0.5216 - val_precision_20: 0.1302 - val_recall_20: 0.8676 - val_f1_score: 0.1513 Epoch 6/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.4632 - categorical_accuracy: 0.5716 - precision_20: 0.1375 - recall_20: 0.9067 - f1_score: 0.1698 - val_loss: 1.6620 - val_categorical_accuracy: 0.5260 - val_precision_20: 0.1305 - val_recall_20: 0.8742 - val_f1_score: 0.1538 Epoch 7/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.4256 - categorical_accuracy: 0.5758 - precision_20: 0.1374 - recall_20: 0.9140 - f1_score: 0.1726 - val_loss: 1.6396 - val_categorical_accuracy: 0.5355 - val_precision_20: 0.1306 - val_recall_20: 0.8781 - val_f1_score: 0.1562 Epoch 8/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.3991 - categorical_accuracy: 0.5874 - precision_20: 0.1373 - recall_20: 0.9207 - f1_score: 0.1751 - val_loss: 1.6238 - val_categorical_accuracy: 0.5388 - val_precision_20: 0.1304 - val_recall_20: 0.8825 - val_f1_score: 0.1585 Epoch 9/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.3774 - categorical_accuracy: 0.5930 - precision_20: 0.1372 - recall_20: 0.9256 - f1_score: 0.1778 - val_loss: 1.6143 - val_categorical_accuracy: 0.5416 - val_precision_20: 0.1312 - val_recall_20: 0.8883 - val_f1_score: 0.1600 Epoch 10/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.3618 - categorical_accuracy: 0.5920 - precision_20: 0.1380 - recall_20: 0.9306 - f1_score: 0.1805 - val_loss: 1.6081 - val_categorical_accuracy: 0.5520 - val_precision_20: 0.1311 - val_recall_20: 0.8908 - val_f1_score: 0.1623 Epoch 11/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.3479 - categorical_accuracy: 0.6008 - precision_20: 0.1379 - recall_20: 0.9332 - f1_score: 0.1834 - val_loss: 1.5988 - val_categorical_accuracy: 0.5544 - val_precision_20: 0.1314 - val_recall_20: 0.8950 - val_f1_score: 0.1637 Epoch 12/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.3354 - categorical_accuracy: 0.6042 - precision_20: 0.1386 - recall_20: 0.9369 - f1_score: 0.1868 - val_loss: 1.6010 - val_categorical_accuracy: 0.5499 - val_precision_20: 0.1312 - val_recall_20: 0.8982 - val_f1_score: 0.1652 Epoch 13/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.3268 - categorical_accuracy: 0.6046 - precision_20: 0.1378 - recall_20: 0.9384 - f1_score: 0.1872 - val_loss: 1.6023 - val_categorical_accuracy: 0.5502 - val_precision_20: 0.1321 - val_recall_20: 0.9020 - val_f1_score: 0.1681 Epoch 14/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.3173 - categorical_accuracy: 0.6065 - precision_20: 0.1382 - recall_20: 0.9406 - f1_score: 0.1898 - val_loss: 1.5910 - val_categorical_accuracy: 0.5587 - val_precision_20: 0.1318 - val_recall_20: 0.9036 - val_f1_score: 0.1676 Epoch 15/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.3098 - categorical_accuracy: 0.6071 - precision_20: 0.1384 - recall_20: 0.9427 - f1_score: 0.1918 - val_loss: 1.6030 - val_categorical_accuracy: 0.5512 - val_precision_20: 0.1316 - val_recall_20: 0.9053 - val_f1_score: 0.1694 Epoch 16/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.3056 - categorical_accuracy: 0.6070 - precision_20: 0.1380 - recall_20: 0.9436 - f1_score: 0.1930 - val_loss: 1.5983 - val_categorical_accuracy: 0.5620 - val_precision_20: 0.1310 - val_recall_20: 0.9056 - val_f1_score: 0.1700 Epoch 17/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.2992 - categorical_accuracy: 0.6147 - precision_20: 0.1384 - recall_20: 0.9450 - f1_score: 0.1934 - val_loss: 1.5963 - val_categorical_accuracy: 0.5647 - val_precision_20: 0.1314 - val_recall_20: 0.9077 - val_f1_score: 0.1714 Epoch 18/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.2947 - categorical_accuracy: 0.6140 - precision_20: 0.1382 - recall_20: 0.9448 - f1_score: 0.1944 - val_loss: 1.5936 - val_categorical_accuracy: 0.5641 - val_precision_20: 0.1313 - val_recall_20: 0.9090 - val_f1_score: 0.1726 Epoch 19/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.2885 - categorical_accuracy: 0.6174 - precision_20: 0.1380 - recall_20: 0.9465 - f1_score: 0.1950 - val_loss: 1.6004 - val_categorical_accuracy: 0.5658 - val_precision_20: 0.1314 - val_recall_20: 0.9100 - val_f1_score: 0.1729 Epoch 20/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.2858 - categorical_accuracy: 0.6163 - precision_20: 0.1383 - recall_20: 0.9463 - f1_score: 0.1964 - val_loss: 1.5978 - val_categorical_accuracy: 0.5662 - val_precision_20: 0.1316 - val_recall_20: 0.9123 - val_f1_score: 0.1740 Epoch 21/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.2836 - categorical_accuracy: 0.6179 - precision_20: 0.1382 - recall_20: 0.9473 - f1_score: 0.1960 - val_loss: 1.5993 - val_categorical_accuracy: 0.5715 - val_precision_20: 0.1318 - val_recall_20: 0.9131 - val_f1_score: 0.1751 Epoch 22/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.2792 - categorical_accuracy: 0.6195 - precision_20: 0.1386 - recall_20: 0.9493 - f1_score: 0.1982 - val_loss: 1.5980 - val_categorical_accuracy: 0.5735 - val_precision_20: 0.1315 - val_recall_20: 0.9159 - val_f1_score: 0.1752 Epoch 23/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.2756 - categorical_accuracy: 0.6234 - precision_20: 0.1383 - recall_20: 0.9500 - f1_score: 0.1977 - val_loss: 1.6032 - val_categorical_accuracy: 0.5664 - val_precision_20: 0.1316 - val_recall_20: 0.9176 - val_f1_score: 0.1770 Epoch 24/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.2743 - categorical_accuracy: 0.6209 - precision_20: 0.1379 - recall_20: 0.9495 - f1_score: 0.1990 - val_loss: 1.6018 - val_categorical_accuracy: 0.5725 - val_precision_20: 0.1315 - val_recall_20: 0.9196 - val_f1_score: 0.1760 Epoch 25/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.2705 - categorical_accuracy: 0.6215 - precision_20: 0.1381 - recall_20: 0.9514 - f1_score: 0.1990 - val_loss: 1.6064 - val_categorical_accuracy: 0.5759 - val_precision_20: 0.1313 - val_recall_20: 0.9194 - val_f1_score: 0.1762 Epoch 26/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.2667 - categorical_accuracy: 0.6235 - precision_20: 0.1382 - recall_20: 0.9515 - f1_score: 0.1996 - val_loss: 1.6037 - val_categorical_accuracy: 0.5767 - val_precision_20: 0.1313 - val_recall_20: 0.9212 - val_f1_score: 0.1765 1 64 15 0.0001 189 48 Epoch 1/50 1371/1371 [==============================] - 19s 13ms/step - loss: 2.4716 - categorical_accuracy: 0.3253 - precision_21: 0.0916 - recall_21: 0.7281 - f1_score: 0.1344 - val_loss: 2.1138 - val_categorical_accuracy: 0.4188 - val_precision_21: 0.0964 - val_recall_21: 0.7630 - val_f1_score: 0.1371 Epoch 2/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.9719 - categorical_accuracy: 0.4451 - precision_21: 0.1075 - recall_21: 0.8107 - f1_score: 0.1473 - val_loss: 1.8813 - val_categorical_accuracy: 0.4458 - val_precision_21: 0.1068 - val_recall_21: 0.8036 - val_f1_score: 0.1422 Epoch 3/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.7681 - categorical_accuracy: 0.4830 - precision_21: 0.1189 - recall_21: 0.8565 - f1_score: 0.1539 - val_loss: 1.7289 - val_categorical_accuracy: 0.4710 - val_precision_21: 0.1171 - val_recall_21: 0.8545 - val_f1_score: 0.1491 Epoch 4/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.6340 - categorical_accuracy: 0.5196 - precision_21: 0.1256 - recall_21: 0.8812 - f1_score: 0.1578 - val_loss: 1.6272 - val_categorical_accuracy: 0.5018 - val_precision_21: 0.1227 - val_recall_21: 0.8713 - val_f1_score: 0.1554 Epoch 5/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.5488 - categorical_accuracy: 0.5456 - precision_21: 0.1275 - recall_21: 0.8934 - f1_score: 0.1620 - val_loss: 1.5659 - val_categorical_accuracy: 0.5214 - val_precision_21: 0.1244 - val_recall_21: 0.8861 - val_f1_score: 0.1605 Epoch 6/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.4928 - categorical_accuracy: 0.5609 - precision_21: 0.1297 - recall_21: 0.9049 - f1_score: 0.1678 - val_loss: 1.5305 - val_categorical_accuracy: 0.5374 - val_precision_21: 0.1257 - val_recall_21: 0.8951 - val_f1_score: 0.1647 Epoch 7/50 1371/1371 [==============================] - 16s 12ms/step - loss: 1.4576 - categorical_accuracy: 0.5730 - precision_21: 0.1315 - recall_21: 0.9127 - f1_score: 0.1724 - val_loss: 1.4958 - val_categorical_accuracy: 0.5491 - val_precision_21: 0.1271 - val_recall_21: 0.9031 - val_f1_score: 0.1698 Epoch 8/50 1371/1371 [==============================] - 15s 11ms/step - loss: 1.4271 - categorical_accuracy: 0.5837 - precision_21: 0.1318 - recall_21: 0.9176 - f1_score: 0.1752 - val_loss: 1.4727 - val_categorical_accuracy: 0.5582 - val_precision_21: 0.1282 - val_recall_21: 0.9106 - val_f1_score: 0.1746 Epoch 9/50 1371/1371 [==============================] - 17s 12ms/step - loss: 1.4069 - categorical_accuracy: 0.5856 - precision_21: 0.1324 - recall_21: 0.9223 - f1_score: 0.1785 - val_loss: 1.4498 - val_categorical_accuracy: 0.5600 - val_precision_21: 0.1291 - val_recall_21: 0.9144 - val_f1_score: 0.1768 Epoch 10/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.3896 - categorical_accuracy: 0.5891 - precision_21: 0.1335 - recall_21: 0.9272 - f1_score: 0.1826 - val_loss: 1.4390 - val_categorical_accuracy: 0.5665 - val_precision_21: 0.1298 - val_recall_21: 0.9183 - val_f1_score: 0.1794 Epoch 11/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.3752 - categorical_accuracy: 0.5970 - precision_21: 0.1333 - recall_21: 0.9299 - f1_score: 0.1822 - val_loss: 1.4251 - val_categorical_accuracy: 0.5607 - val_precision_21: 0.1320 - val_recall_21: 0.9250 - val_f1_score: 0.1847 Epoch 12/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.3656 - categorical_accuracy: 0.5927 - precision_21: 0.1340 - recall_21: 0.9341 - f1_score: 0.1888 - val_loss: 1.4076 - val_categorical_accuracy: 0.5736 - val_precision_21: 0.1325 - val_recall_21: 0.9270 - val_f1_score: 0.1878 Epoch 13/50 1371/1371 [==============================] - 17s 13ms/step - loss: 1.3555 - categorical_accuracy: 0.5980 - precision_21: 0.1344 - recall_21: 0.9353 - f1_score: 0.1924 - val_loss: 1.4001 - val_categorical_accuracy: 0.5792 - val_precision_21: 0.1318 - val_recall_21: 0.9305 - val_f1_score: 0.1860 Epoch 14/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.3455 - categorical_accuracy: 0.5998 - precision_21: 0.1344 - recall_21: 0.9377 - f1_score: 0.1929 - val_loss: 1.3941 - val_categorical_accuracy: 0.5777 - val_precision_21: 0.1327 - val_recall_21: 0.9321 - val_f1_score: 0.1883 Epoch 15/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.3386 - categorical_accuracy: 0.6035 - precision_21: 0.1346 - recall_21: 0.9388 - f1_score: 0.2054 - val_loss: 1.3915 - val_categorical_accuracy: 0.5813 - val_precision_21: 0.1325 - val_recall_21: 0.9348 - val_f1_score: 0.1900 Epoch 16/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.3322 - categorical_accuracy: 0.6051 - precision_21: 0.1342 - recall_21: 0.9397 - f1_score: 0.2014 - val_loss: 1.3810 - val_categorical_accuracy: 0.5858 - val_precision_21: 0.1331 - val_recall_21: 0.9366 - val_f1_score: 0.1921 Epoch 17/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.3263 - categorical_accuracy: 0.6085 - precision_21: 0.1349 - recall_21: 0.9420 - f1_score: 0.2052 - val_loss: 1.3743 - val_categorical_accuracy: 0.5908 - val_precision_21: 0.1319 - val_recall_21: 0.9366 - val_f1_score: 0.1917 Epoch 18/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.3210 - categorical_accuracy: 0.6094 - precision_21: 0.1340 - recall_21: 0.9428 - f1_score: 0.2078 - val_loss: 1.3737 - val_categorical_accuracy: 0.5834 - val_precision_21: 0.1330 - val_recall_21: 0.9379 - val_f1_score: 0.1937 Epoch 19/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.3177 - categorical_accuracy: 0.6154 - precision_21: 0.1339 - recall_21: 0.9422 - f1_score: 0.2078 - val_loss: 1.3662 - val_categorical_accuracy: 0.5897 - val_precision_21: 0.1332 - val_recall_21: 0.9413 - val_f1_score: 0.1955 Epoch 20/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.3118 - categorical_accuracy: 0.6166 - precision_21: 0.1345 - recall_21: 0.9440 - f1_score: 0.2103 - val_loss: 1.3623 - val_categorical_accuracy: 0.5874 - val_precision_21: 0.1324 - val_recall_21: 0.9407 - val_f1_score: 0.1945 Epoch 21/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.3101 - categorical_accuracy: 0.6133 - precision_21: 0.1333 - recall_21: 0.9441 - f1_score: 0.2119 - val_loss: 1.3612 - val_categorical_accuracy: 0.5865 - val_precision_21: 0.1331 - val_recall_21: 0.9418 - val_f1_score: 0.1953 Epoch 22/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.3058 - categorical_accuracy: 0.6183 - precision_21: 0.1337 - recall_21: 0.9445 - f1_score: 0.2106 - val_loss: 1.3586 - val_categorical_accuracy: 0.5873 - val_precision_21: 0.1332 - val_recall_21: 0.9438 - val_f1_score: 0.1954 2 64 15 0.0001 190 47 Epoch 1/50 1304/1304 [==============================] - 19s 14ms/step - loss: 2.4586 - categorical_accuracy: 0.3377 - precision_22: 0.0979 - recall_22: 0.7183 - f1_score: 0.1366 - val_loss: 2.3886 - val_categorical_accuracy: 0.3301 - val_precision_22: 0.0971 - val_recall_22: 0.7057 - val_f1_score: 0.1317 Epoch 2/50 1304/1304 [==============================] - 18s 14ms/step - loss: 1.9269 - categorical_accuracy: 0.4644 - precision_22: 0.1166 - recall_22: 0.8035 - f1_score: 0.1418 - val_loss: 2.1984 - val_categorical_accuracy: 0.3800 - val_precision_22: 0.1073 - val_recall_22: 0.7458 - val_f1_score: 0.1370 Epoch 3/50 1304/1304 [==============================] - 18s 14ms/step - loss: 1.7148 - categorical_accuracy: 0.5137 - precision_22: 0.1299 - recall_22: 0.8563 - f1_score: 0.1490 - val_loss: 2.1056 - val_categorical_accuracy: 0.4089 - val_precision_22: 0.1164 - val_recall_22: 0.7689 - val_f1_score: 0.1425 Epoch 4/50 1304/1304 [==============================] - 18s 14ms/step - loss: 1.5816 - categorical_accuracy: 0.5439 - precision_22: 0.1395 - recall_22: 0.8882 - f1_score: 0.1541 - val_loss: 2.0680 - val_categorical_accuracy: 0.4260 - val_precision_22: 0.1213 - val_recall_22: 0.7805 - val_f1_score: 0.1443 Epoch 5/50 1304/1304 [==============================] - 15s 11ms/step - loss: 1.4986 - categorical_accuracy: 0.5664 - precision_22: 0.1434 - recall_22: 0.9018 - f1_score: 0.1603 - val_loss: 2.0538 - val_categorical_accuracy: 0.4398 - val_precision_22: 0.1238 - val_recall_22: 0.7994 - val_f1_score: 0.1472 Epoch 6/50 1304/1304 [==============================] - 15s 12ms/step - loss: 1.4469 - categorical_accuracy: 0.5795 - precision_22: 0.1427 - recall_22: 0.9120 - f1_score: 0.1635 - val_loss: 2.0530 - val_categorical_accuracy: 0.4451 - val_precision_22: 0.1265 - val_recall_22: 0.8214 - val_f1_score: 0.1512 Epoch 7/50 1304/1304 [==============================] - 15s 11ms/step - loss: 1.4089 - categorical_accuracy: 0.5848 - precision_22: 0.1438 - recall_22: 0.9179 - f1_score: 0.1684 - val_loss: 2.0430 - val_categorical_accuracy: 0.4561 - val_precision_22: 0.1270 - val_recall_22: 0.8372 - val_f1_score: 0.1536 Epoch 8/50 1304/1304 [==============================] - 15s 11ms/step - loss: 1.3840 - categorical_accuracy: 0.5855 - precision_22: 0.1419 - recall_22: 0.9268 - f1_score: 0.1716 - val_loss: 2.0567 - val_categorical_accuracy: 0.4577 - val_precision_22: 0.1275 - val_recall_22: 0.8484 - val_f1_score: 0.1550 Epoch 9/50 1304/1304 [==============================] - 15s 11ms/step - loss: 1.3640 - categorical_accuracy: 0.5952 - precision_22: 0.1413 - recall_22: 0.9312 - f1_score: 0.1767 - val_loss: 2.0595 - val_categorical_accuracy: 0.4543 - val_precision_22: 0.1278 - val_recall_22: 0.8599 - val_f1_score: 0.1559 Epoch 10/50 1304/1304 [==============================] - 15s 11ms/step - loss: 1.3457 - categorical_accuracy: 0.5935 - precision_22: 0.1402 - recall_22: 0.9368 - f1_score: 0.1787 - val_loss: 2.0619 - val_categorical_accuracy: 0.4632 - val_precision_22: 0.1269 - val_recall_22: 0.8668 - val_f1_score: 0.1559 Epoch 11/50 1304/1304 [==============================] - 16s 12ms/step - loss: 1.3332 - categorical_accuracy: 0.6019 - precision_22: 0.1400 - recall_22: 0.9406 - f1_score: 0.1829 - val_loss: 2.0702 - val_categorical_accuracy: 0.4633 - val_precision_22: 0.1270 - val_recall_22: 0.8718 - val_f1_score: 0.1577 Epoch 12/50 1304/1304 [==============================] - 17s 13ms/step - loss: 1.3222 - categorical_accuracy: 0.6036 - precision_22: 0.1384 - recall_22: 0.9436 - f1_score: 0.1859 - val_loss: 2.0896 - val_categorical_accuracy: 0.4641 - val_precision_22: 0.1278 - val_recall_22: 0.8780 - val_f1_score: 0.1568 Epoch 13/50 1304/1304 [==============================] - 17s 13ms/step - loss: 1.3136 - categorical_accuracy: 0.6057 - precision_22: 0.1380 - recall_22: 0.9462 - f1_score: 0.1880 - val_loss: 2.0810 - val_categorical_accuracy: 0.4644 - val_precision_22: 0.1274 - val_recall_22: 0.8803 - val_f1_score: 0.1590 Epoch 14/50 1304/1304 [==============================] - 17s 13ms/step - loss: 1.3043 - categorical_accuracy: 0.6101 - precision_22: 0.1379 - recall_22: 0.9485 - f1_score: 0.1881 - val_loss: 2.0983 - val_categorical_accuracy: 0.4657 - val_precision_22: 0.1266 - val_recall_22: 0.8804 - val_f1_score: 0.1580 Epoch 15/50 1304/1304 [==============================] - 17s 13ms/step - loss: 1.3000 - categorical_accuracy: 0.6105 - precision_22: 0.1380 - recall_22: 0.9498 - f1_score: 0.1911 - val_loss: 2.1009 - val_categorical_accuracy: 0.4749 - val_precision_22: 0.1259 - val_recall_22: 0.8832 - val_f1_score: 0.1582 Epoch 16/50 1304/1304 [==============================] - 17s 13ms/step - loss: 1.2928 - categorical_accuracy: 0.6131 - precision_22: 0.1366 - recall_22: 0.9523 - f1_score: 0.1934 - val_loss: 2.1195 - val_categorical_accuracy: 0.4737 - val_precision_22: 0.1249 - val_recall_22: 0.8870 - val_f1_score: 0.1588 3 64 15 0.0001 190 47 Epoch 1/50 1387/1387 [==============================] - 19s 13ms/step - loss: 2.6039 - categorical_accuracy: 0.2928 - precision_23: 0.0848 - recall_23: 0.6722 - f1_score: 0.1246 - val_loss: 2.1040 - val_categorical_accuracy: 0.4028 - val_precision_23: 0.1040 - val_recall_23: 0.7876 - val_f1_score: 0.1408 Epoch 2/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.9701 - categorical_accuracy: 0.4323 - precision_23: 0.1068 - recall_23: 0.8150 - f1_score: 0.1457 - val_loss: 1.7780 - val_categorical_accuracy: 0.5170 - val_precision_23: 0.1114 - val_recall_23: 0.8542 - val_f1_score: 0.1469 Epoch 3/50 1387/1387 [==============================] - 17s 13ms/step - loss: 1.7457 - categorical_accuracy: 0.4991 - precision_23: 0.1146 - recall_23: 0.8617 - f1_score: 0.1506 - val_loss: 1.6334 - val_categorical_accuracy: 0.5547 - val_precision_23: 0.1181 - val_recall_23: 0.8766 - val_f1_score: 0.1484 Epoch 4/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.6182 - categorical_accuracy: 0.5282 - precision_23: 0.1198 - recall_23: 0.8818 - f1_score: 0.1531 - val_loss: 1.5422 - val_categorical_accuracy: 0.5757 - val_precision_23: 0.1233 - val_recall_23: 0.8914 - val_f1_score: 0.1520 Epoch 5/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.5380 - categorical_accuracy: 0.5495 - precision_23: 0.1241 - recall_23: 0.8979 - f1_score: 0.1575 - val_loss: 1.4905 - val_categorical_accuracy: 0.5826 - val_precision_23: 0.1239 - val_recall_23: 0.9012 - val_f1_score: 0.1540 Epoch 6/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.4840 - categorical_accuracy: 0.5629 - precision_23: 0.1237 - recall_23: 0.9111 - f1_score: 0.1601 - val_loss: 1.4527 - val_categorical_accuracy: 0.5891 - val_precision_23: 0.1246 - val_recall_23: 0.9082 - val_f1_score: 0.1560 Epoch 7/50 1387/1387 [==============================] - 17s 13ms/step - loss: 1.4476 - categorical_accuracy: 0.5690 - precision_23: 0.1253 - recall_23: 0.9202 - f1_score: 0.1644 - val_loss: 1.4354 - val_categorical_accuracy: 0.5939 - val_precision_23: 0.1236 - val_recall_23: 0.9124 - val_f1_score: 0.1599 Epoch 8/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.4188 - categorical_accuracy: 0.5768 - precision_23: 0.1249 - recall_23: 0.9306 - f1_score: 0.1665 - val_loss: 1.4261 - val_categorical_accuracy: 0.5924 - val_precision_23: 0.1240 - val_recall_23: 0.9198 - val_f1_score: 0.1639 Epoch 9/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.3998 - categorical_accuracy: 0.5787 - precision_23: 0.1254 - recall_23: 0.9351 - f1_score: 0.1715 - val_loss: 1.4137 - val_categorical_accuracy: 0.5929 - val_precision_23: 0.1244 - val_recall_23: 0.9245 - val_f1_score: 0.1673 Epoch 10/50 1387/1387 [==============================] - 17s 13ms/step - loss: 1.3814 - categorical_accuracy: 0.5812 - precision_23: 0.1258 - recall_23: 0.9401 - f1_score: 0.1716 - val_loss: 1.4023 - val_categorical_accuracy: 0.5981 - val_precision_23: 0.1251 - val_recall_23: 0.9272 - val_f1_score: 0.1718 Epoch 11/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.3672 - categorical_accuracy: 0.5933 - precision_23: 0.1259 - recall_23: 0.9449 - f1_score: 0.1793 - val_loss: 1.3959 - val_categorical_accuracy: 0.5969 - val_precision_23: 0.1252 - val_recall_23: 0.9284 - val_f1_score: 0.1741 Epoch 12/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.3581 - categorical_accuracy: 0.5903 - precision_23: 0.1271 - recall_23: 0.9481 - f1_score: 0.1806 - val_loss: 1.3860 - val_categorical_accuracy: 0.6019 - val_precision_23: 0.1247 - val_recall_23: 0.9299 - val_f1_score: 0.1756 Epoch 13/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.3486 - categorical_accuracy: 0.5952 - precision_23: 0.1272 - recall_23: 0.9501 - f1_score: 0.1854 - val_loss: 1.3823 - val_categorical_accuracy: 0.6080 - val_precision_23: 0.1247 - val_recall_23: 0.9306 - val_f1_score: 0.1763 Epoch 14/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.3393 - categorical_accuracy: 0.5965 - precision_23: 0.1274 - recall_23: 0.9535 - f1_score: 0.1871 - val_loss: 1.3774 - val_categorical_accuracy: 0.6112 - val_precision_23: 0.1252 - val_recall_23: 0.9312 - val_f1_score: 0.1776 Epoch 15/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.3326 - categorical_accuracy: 0.6001 - precision_23: 0.1275 - recall_23: 0.9539 - f1_score: 0.1890 - val_loss: 1.3691 - val_categorical_accuracy: 0.6143 - val_precision_23: 0.1258 - val_recall_23: 0.9328 - val_f1_score: 0.1799 Epoch 16/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.3266 - categorical_accuracy: 0.5972 - precision_23: 0.1279 - recall_23: 0.9567 - f1_score: 0.1900 - val_loss: 1.3711 - val_categorical_accuracy: 0.6080 - val_precision_23: 0.1260 - val_recall_23: 0.9334 - val_f1_score: 0.1810 Epoch 17/50 1387/1387 [==============================] - 17s 13ms/step - loss: 1.3206 - categorical_accuracy: 0.5987 - precision_23: 0.1283 - recall_23: 0.9571 - f1_score: 0.1909 - val_loss: 1.3655 - val_categorical_accuracy: 0.6157 - val_precision_23: 0.1262 - val_recall_23: 0.9341 - val_f1_score: 0.1824 Epoch 18/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.3170 - categorical_accuracy: 0.6048 - precision_23: 0.1282 - recall_23: 0.9592 - f1_score: 0.1923 - val_loss: 1.3635 - val_categorical_accuracy: 0.6174 - val_precision_23: 0.1259 - val_recall_23: 0.9347 - val_f1_score: 0.1835 Epoch 19/50 1387/1387 [==============================] - 17s 13ms/step - loss: 1.3116 - categorical_accuracy: 0.6039 - precision_23: 0.1284 - recall_23: 0.9596 - f1_score: 0.1926 - val_loss: 1.3698 - val_categorical_accuracy: 0.6111 - val_precision_23: 0.1266 - val_recall_23: 0.9352 - val_f1_score: 0.1852 Epoch 20/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.3063 - categorical_accuracy: 0.6074 - precision_23: 0.1288 - recall_23: 0.9624 - f1_score: 0.1954 - val_loss: 1.3714 - val_categorical_accuracy: 0.6071 - val_precision_23: 0.1263 - val_recall_23: 0.9362 - val_f1_score: 0.1853 Epoch 21/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.3046 - categorical_accuracy: 0.6083 - precision_23: 0.1285 - recall_23: 0.9604 - f1_score: 0.1955 - val_loss: 1.3642 - val_categorical_accuracy: 0.6172 - val_precision_23: 0.1257 - val_recall_23: 0.9369 - val_f1_score: 0.1840 Epoch 22/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.2983 - categorical_accuracy: 0.6100 - precision_23: 0.1288 - recall_23: 0.9627 - f1_score: 0.1961 - val_loss: 1.3610 - val_categorical_accuracy: 0.6207 - val_precision_23: 0.1260 - val_recall_23: 0.9363 - val_f1_score: 0.1850 Epoch 23/50 1387/1387 [==============================] - 17s 13ms/step - loss: 1.2983 - categorical_accuracy: 0.6082 - precision_23: 0.1286 - recall_23: 0.9626 - f1_score: 0.1962 - val_loss: 1.3569 - val_categorical_accuracy: 0.6158 - val_precision_23: 0.1260 - val_recall_23: 0.9363 - val_f1_score: 0.1851 4 64 15 0.0001 190 47 Epoch 1/50 1387/1387 [==============================] - 19s 13ms/step - loss: 2.6656 - categorical_accuracy: 0.2583 - precision_24: 0.0840 - recall_24: 0.6897 - f1_score: 0.1268 - val_loss: 2.0798 - val_categorical_accuracy: 0.4219 - val_precision_24: 0.1053 - val_recall_24: 0.8052 - val_f1_score: 0.1410 Epoch 2/50 1387/1387 [==============================] - 17s 12ms/step - loss: 2.0125 - categorical_accuracy: 0.4298 - precision_24: 0.1055 - recall_24: 0.8108 - f1_score: 0.1455 - val_loss: 1.7794 - val_categorical_accuracy: 0.5001 - val_precision_24: 0.1159 - val_recall_24: 0.8431 - val_f1_score: 0.1421 Epoch 3/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.7916 - categorical_accuracy: 0.4882 - precision_24: 0.1173 - recall_24: 0.8445 - f1_score: 0.1486 - val_loss: 1.6377 - val_categorical_accuracy: 0.5345 - val_precision_24: 0.1282 - val_recall_24: 0.8871 - val_f1_score: 0.1496 Epoch 4/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.6602 - categorical_accuracy: 0.5243 - precision_24: 0.1244 - recall_24: 0.8734 - f1_score: 0.1541 - val_loss: 1.5492 - val_categorical_accuracy: 0.5545 - val_precision_24: 0.1296 - val_recall_24: 0.9023 - val_f1_score: 0.1527 Epoch 5/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.5749 - categorical_accuracy: 0.5477 - precision_24: 0.1253 - recall_24: 0.8912 - f1_score: 0.1577 - val_loss: 1.4883 - val_categorical_accuracy: 0.5648 - val_precision_24: 0.1295 - val_recall_24: 0.9134 - val_f1_score: 0.1550 Epoch 6/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.5195 - categorical_accuracy: 0.5598 - precision_24: 0.1263 - recall_24: 0.9065 - f1_score: 0.1616 - val_loss: 1.4487 - val_categorical_accuracy: 0.5735 - val_precision_24: 0.1307 - val_recall_24: 0.9208 - val_f1_score: 0.1587 Epoch 7/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.4800 - categorical_accuracy: 0.5674 - precision_24: 0.1282 - recall_24: 0.9167 - f1_score: 0.1662 - val_loss: 1.4220 - val_categorical_accuracy: 0.5790 - val_precision_24: 0.1300 - val_recall_24: 0.9258 - val_f1_score: 0.1618 Epoch 8/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.4472 - categorical_accuracy: 0.5740 - precision_24: 0.1284 - recall_24: 0.9257 - f1_score: 0.1720 - val_loss: 1.3991 - val_categorical_accuracy: 0.5872 - val_precision_24: 0.1306 - val_recall_24: 0.9292 - val_f1_score: 0.1644 Epoch 9/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.4256 - categorical_accuracy: 0.5789 - precision_24: 0.1287 - recall_24: 0.9306 - f1_score: 0.1744 - val_loss: 1.3859 - val_categorical_accuracy: 0.5877 - val_precision_24: 0.1310 - val_recall_24: 0.9317 - val_f1_score: 0.1649 Epoch 10/50 1387/1387 [==============================] - 17s 13ms/step - loss: 1.4054 - categorical_accuracy: 0.5803 - precision_24: 0.1295 - recall_24: 0.9353 - f1_score: 0.1798 - val_loss: 1.3812 - val_categorical_accuracy: 0.5867 - val_precision_24: 0.1312 - val_recall_24: 0.9347 - val_f1_score: 0.1675 Epoch 11/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.3923 - categorical_accuracy: 0.5860 - precision_24: 0.1296 - recall_24: 0.9385 - f1_score: 0.1806 - val_loss: 1.3631 - val_categorical_accuracy: 0.5946 - val_precision_24: 0.1314 - val_recall_24: 0.9357 - val_f1_score: 0.1698 Epoch 12/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.3804 - categorical_accuracy: 0.5884 - precision_24: 0.1300 - recall_24: 0.9411 - f1_score: 0.1858 - val_loss: 1.3576 - val_categorical_accuracy: 0.5972 - val_precision_24: 0.1305 - val_recall_24: 0.9373 - val_f1_score: 0.1769 Epoch 13/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.3691 - categorical_accuracy: 0.5962 - precision_24: 0.1292 - recall_24: 0.9418 - f1_score: 0.1874 - val_loss: 1.3499 - val_categorical_accuracy: 0.5984 - val_precision_24: 0.1315 - val_recall_24: 0.9402 - val_f1_score: 0.1818 Epoch 14/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.3603 - categorical_accuracy: 0.5949 - precision_24: 0.1298 - recall_24: 0.9457 - f1_score: 0.1890 - val_loss: 1.3394 - val_categorical_accuracy: 0.6009 - val_precision_24: 0.1312 - val_recall_24: 0.9418 - val_f1_score: 0.1828 Epoch 15/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.3531 - categorical_accuracy: 0.5949 - precision_24: 0.1296 - recall_24: 0.9475 - f1_score: 0.1954 - val_loss: 1.3357 - val_categorical_accuracy: 0.6035 - val_precision_24: 0.1310 - val_recall_24: 0.9431 - val_f1_score: 0.1821 Epoch 16/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.3472 - categorical_accuracy: 0.5995 - precision_24: 0.1293 - recall_24: 0.9482 - f1_score: 0.1986 - val_loss: 1.3283 - val_categorical_accuracy: 0.6056 - val_precision_24: 0.1303 - val_recall_24: 0.9433 - val_f1_score: 0.1813 Epoch 17/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.3404 - categorical_accuracy: 0.5990 - precision_24: 0.1295 - recall_24: 0.9504 - f1_score: 0.1982 - val_loss: 1.3309 - val_categorical_accuracy: 0.6076 - val_precision_24: 0.1296 - val_recall_24: 0.9437 - val_f1_score: 0.1804 0 64 15 0.001 189 48 Epoch 1/50
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5. warnings.warn(
1367/1367 [==============================] - 19s 13ms/step - loss: 1.7370 - categorical_accuracy: 0.4994 - precision_25: 0.1245 - recall_25: 0.8601 - f1_score: 0.1576 - val_loss: 1.6419 - val_categorical_accuracy: 0.5438 - val_precision_25: 0.1282 - val_recall_25: 0.9136 - val_f1_score: 0.1645 Epoch 2/50 1367/1367 [==============================] - 17s 13ms/step - loss: 1.4032 - categorical_accuracy: 0.5844 - precision_25: 0.1306 - recall_25: 0.9371 - f1_score: 0.1783 - val_loss: 1.6448 - val_categorical_accuracy: 0.5102 - val_precision_25: 0.1312 - val_recall_25: 0.9236 - val_f1_score: 0.1712 Epoch 3/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.3638 - categorical_accuracy: 0.5918 - precision_25: 0.1311 - recall_25: 0.9454 - f1_score: 0.1878 - val_loss: 1.6282 - val_categorical_accuracy: 0.5192 - val_precision_25: 0.1305 - val_recall_25: 0.9319 - val_f1_score: 0.1721 Epoch 4/50 1367/1367 [==============================] - 17s 13ms/step - loss: 1.3382 - categorical_accuracy: 0.6092 - precision_25: 0.1311 - recall_25: 0.9525 - f1_score: 0.1946 - val_loss: 1.6145 - val_categorical_accuracy: 0.5511 - val_precision_25: 0.1256 - val_recall_25: 0.9322 - val_f1_score: 0.1787 Epoch 5/50 1367/1367 [==============================] - 17s 13ms/step - loss: 1.3254 - categorical_accuracy: 0.6049 - precision_25: 0.1305 - recall_25: 0.9548 - f1_score: 0.1987 - val_loss: 1.5625 - val_categorical_accuracy: 0.5893 - val_precision_25: 0.1271 - val_recall_25: 0.9387 - val_f1_score: 0.1806 Epoch 6/50 1367/1367 [==============================] - 17s 13ms/step - loss: 1.3277 - categorical_accuracy: 0.6144 - precision_25: 0.1297 - recall_25: 0.9532 - f1_score: 0.1998 - val_loss: 1.6057 - val_categorical_accuracy: 0.5736 - val_precision_25: 0.1240 - val_recall_25: 0.9336 - val_f1_score: 0.1757 Epoch 7/50 1367/1367 [==============================] - 17s 13ms/step - loss: 1.3060 - categorical_accuracy: 0.6233 - precision_25: 0.1299 - recall_25: 0.9588 - f1_score: 0.2044 - val_loss: 1.5539 - val_categorical_accuracy: 0.5837 - val_precision_25: 0.1246 - val_recall_25: 0.9399 - val_f1_score: 0.1795 Epoch 8/50 1367/1367 [==============================] - 17s 13ms/step - loss: 1.3033 - categorical_accuracy: 0.6301 - precision_25: 0.1294 - recall_25: 0.9550 - f1_score: 0.2028 - val_loss: 1.5692 - val_categorical_accuracy: 0.5831 - val_precision_25: 0.1260 - val_recall_25: 0.9348 - val_f1_score: 0.1848 Epoch 9/50 1367/1367 [==============================] - 17s 13ms/step - loss: 1.2952 - categorical_accuracy: 0.6233 - precision_25: 0.1309 - recall_25: 0.9572 - f1_score: 0.2122 - val_loss: 1.6200 - val_categorical_accuracy: 0.5490 - val_precision_25: 0.1288 - val_recall_25: 0.9454 - val_f1_score: 0.1876 Epoch 10/50 1367/1367 [==============================] - 17s 13ms/step - loss: 1.2904 - categorical_accuracy: 0.6218 - precision_25: 0.1311 - recall_25: 0.9555 - f1_score: 0.2119 - val_loss: 1.6166 - val_categorical_accuracy: 0.5655 - val_precision_25: 0.1282 - val_recall_25: 0.9405 - val_f1_score: 0.1822 Epoch 11/50 1367/1367 [==============================] - 17s 13ms/step - loss: 1.2874 - categorical_accuracy: 0.6267 - precision_25: 0.1318 - recall_25: 0.9551 - f1_score: 0.2151 - val_loss: 1.5793 - val_categorical_accuracy: 0.5738 - val_precision_25: 0.1269 - val_recall_25: 0.9378 - val_f1_score: 0.1783 Epoch 12/50 1367/1367 [==============================] - 17s 13ms/step - loss: 1.2906 - categorical_accuracy: 0.6262 - precision_25: 0.1324 - recall_25: 0.9516 - f1_score: 0.2161 - val_loss: 1.5970 - val_categorical_accuracy: 0.5811 - val_precision_25: 0.1275 - val_recall_25: 0.9465 - val_f1_score: 0.1849 1 64 15 0.001 189 48 Epoch 1/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.8104 - categorical_accuracy: 0.4827 - precision_26: 0.1229 - recall_26: 0.8523 - f1_score: 0.1526 - val_loss: 1.4663 - val_categorical_accuracy: 0.5501 - val_precision_26: 0.1267 - val_recall_26: 0.9200 - val_f1_score: 0.1663 Epoch 2/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.4272 - categorical_accuracy: 0.5824 - precision_26: 0.1366 - recall_26: 0.9306 - f1_score: 0.1819 - val_loss: 1.4190 - val_categorical_accuracy: 0.5952 - val_precision_26: 0.1316 - val_recall_26: 0.9374 - val_f1_score: 0.1856 Epoch 3/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.3986 - categorical_accuracy: 0.5838 - precision_26: 0.1320 - recall_26: 0.9393 - f1_score: 0.2014 - val_loss: 1.3773 - val_categorical_accuracy: 0.5909 - val_precision_26: 0.1346 - val_recall_26: 0.9434 - val_f1_score: 0.1950 Epoch 4/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.3658 - categorical_accuracy: 0.5985 - precision_26: 0.1342 - recall_26: 0.9442 - f1_score: 0.2116 - val_loss: 1.3968 - val_categorical_accuracy: 0.6128 - val_precision_26: 0.1381 - val_recall_26: 0.9467 - val_f1_score: 0.2055 Epoch 5/50 1371/1371 [==============================] - 18s 13ms/step - loss: 1.3623 - categorical_accuracy: 0.5975 - precision_26: 0.1347 - recall_26: 0.9444 - f1_score: 0.2168 - val_loss: 1.3735 - val_categorical_accuracy: 0.5830 - val_precision_26: 0.1333 - val_recall_26: 0.9509 - val_f1_score: 0.1990 Epoch 6/50 1371/1371 [==============================] - 17s 13ms/step - loss: 1.3457 - categorical_accuracy: 0.6054 - precision_26: 0.1337 - recall_26: 0.9482 - f1_score: 0.2254 - val_loss: 1.3706 - val_categorical_accuracy: 0.6215 - val_precision_26: 0.1315 - val_recall_26: 0.9402 - val_f1_score: 0.1924 Epoch 7/50 1371/1371 [==============================] - 17s 13ms/step - loss: 1.3412 - categorical_accuracy: 0.6076 - precision_26: 0.1344 - recall_26: 0.9475 - f1_score: 0.2308 - val_loss: 1.3447 - val_categorical_accuracy: 0.6353 - val_precision_26: 0.1354 - val_recall_26: 0.9393 - val_f1_score: 0.2040 2 64 15 0.001 190 47 Epoch 1/50 1304/1304 [==============================] - 19s 14ms/step - loss: 1.7553 - categorical_accuracy: 0.4993 - precision_27: 0.1276 - recall_27: 0.8447 - f1_score: 0.1500 - val_loss: 2.0312 - val_categorical_accuracy: 0.4775 - val_precision_27: 0.1228 - val_recall_27: 0.8339 - val_f1_score: 0.1478 Epoch 2/50 1304/1304 [==============================] - 18s 14ms/step - loss: 1.4024 - categorical_accuracy: 0.5822 - precision_27: 0.1379 - recall_27: 0.9279 - f1_score: 0.1766 - val_loss: 2.1022 - val_categorical_accuracy: 0.4573 - val_precision_27: 0.1205 - val_recall_27: 0.8548 - val_f1_score: 0.1536 Epoch 3/50 1304/1304 [==============================] - 18s 14ms/step - loss: 1.3509 - categorical_accuracy: 0.5944 - precision_27: 0.1344 - recall_27: 0.9382 - f1_score: 0.1905 - val_loss: 2.0769 - val_categorical_accuracy: 0.4743 - val_precision_27: 0.1204 - val_recall_27: 0.8702 - val_f1_score: 0.1542 Epoch 4/50 1304/1304 [==============================] - 18s 14ms/step - loss: 1.3311 - categorical_accuracy: 0.6144 - precision_27: 0.1351 - recall_27: 0.9434 - f1_score: 0.2012 - val_loss: 1.9735 - val_categorical_accuracy: 0.4768 - val_precision_27: 0.1254 - val_recall_27: 0.9087 - val_f1_score: 0.1711 Epoch 5/50 1304/1304 [==============================] - 17s 13ms/step - loss: 1.3197 - categorical_accuracy: 0.6099 - precision_27: 0.1331 - recall_27: 0.9493 - f1_score: 0.2023 - val_loss: 2.1227 - val_categorical_accuracy: 0.4719 - val_precision_27: 0.1214 - val_recall_27: 0.8706 - val_f1_score: 0.1603 Epoch 6/50 1304/1304 [==============================] - 17s 13ms/step - loss: 1.3131 - categorical_accuracy: 0.6152 - precision_27: 0.1345 - recall_27: 0.9442 - f1_score: 0.2054 - val_loss: 2.0533 - val_categorical_accuracy: 0.4887 - val_precision_27: 0.1239 - val_recall_27: 0.9126 - val_f1_score: 0.1658 Epoch 7/50 1304/1304 [==============================] - 17s 13ms/step - loss: 1.2993 - categorical_accuracy: 0.6168 - precision_27: 0.1343 - recall_27: 0.9490 - f1_score: 0.2078 - val_loss: 2.1295 - val_categorical_accuracy: 0.4678 - val_precision_27: 0.1261 - val_recall_27: 0.8967 - val_f1_score: 0.1655 3 64 15 0.001 190 47 Epoch 1/50 1387/1387 [==============================] - 19s 13ms/step - loss: 1.7735 - categorical_accuracy: 0.4939 - precision_28: 0.1216 - recall_28: 0.8582 - f1_score: 0.1547 - val_loss: 1.4852 - val_categorical_accuracy: 0.5848 - val_precision_28: 0.1272 - val_recall_28: 0.9095 - val_f1_score: 0.1648 Epoch 2/50 1387/1387 [==============================] - 18s 13ms/step - loss: 1.4354 - categorical_accuracy: 0.5707 - precision_28: 0.1319 - recall_28: 0.9430 - f1_score: 0.1834 - val_loss: 1.4334 - val_categorical_accuracy: 0.5862 - val_precision_28: 0.1312 - val_recall_28: 0.9329 - val_f1_score: 0.1812 Epoch 3/50 1387/1387 [==============================] - 17s 13ms/step - loss: 1.3804 - categorical_accuracy: 0.5930 - precision_28: 0.1324 - recall_28: 0.9491 - f1_score: 0.2017 - val_loss: 1.4276 - val_categorical_accuracy: 0.5913 - val_precision_28: 0.1305 - val_recall_28: 0.9374 - val_f1_score: 0.1918 Epoch 4/50 1387/1387 [==============================] - 17s 13ms/step - loss: 1.3683 - categorical_accuracy: 0.5888 - precision_28: 0.1307 - recall_28: 0.9518 - f1_score: 0.1992 - val_loss: 1.3629 - val_categorical_accuracy: 0.6399 - val_precision_28: 0.1288 - val_recall_28: 0.9315 - val_f1_score: 0.1840 Epoch 5/50 1387/1387 [==============================] - 18s 13ms/step - loss: 1.3355 - categorical_accuracy: 0.6097 - precision_28: 0.1314 - recall_28: 0.9559 - f1_score: 0.2043 - val_loss: 1.4246 - val_categorical_accuracy: 0.6016 - val_precision_28: 0.1300 - val_recall_28: 0.9404 - val_f1_score: 0.1994 Epoch 6/50 1387/1387 [==============================] - 18s 13ms/step - loss: 1.3443 - categorical_accuracy: 0.5955 - precision_28: 0.1304 - recall_28: 0.9575 - f1_score: 0.2052 - val_loss: 1.4210 - val_categorical_accuracy: 0.5904 - val_precision_28: 0.1322 - val_recall_28: 0.9411 - val_f1_score: 0.1990 Epoch 7/50 1387/1387 [==============================] - 18s 13ms/step - loss: 1.3356 - categorical_accuracy: 0.6071 - precision_28: 0.1302 - recall_28: 0.9553 - f1_score: 0.2079 - val_loss: 1.3235 - val_categorical_accuracy: 0.6253 - val_precision_28: 0.1329 - val_recall_28: 0.9398 - val_f1_score: 0.1975 Epoch 8/50 1387/1387 [==============================] - 18s 13ms/step - loss: 1.3271 - categorical_accuracy: 0.6076 - precision_28: 0.1313 - recall_28: 0.9568 - f1_score: 0.2122 - val_loss: 1.3706 - val_categorical_accuracy: 0.5701 - val_precision_28: 0.1296 - val_recall_28: 0.9378 - val_f1_score: 0.1943 4 64 15 0.001 190 47 Epoch 1/50 1387/1387 [==============================] - 19s 13ms/step - loss: 1.8188 - categorical_accuracy: 0.4767 - precision_29: 0.1266 - recall_29: 0.8543 - f1_score: 0.1588 - val_loss: 1.4056 - val_categorical_accuracy: 0.5757 - val_precision_29: 0.1440 - val_recall_29: 0.9354 - val_f1_score: 0.1719 Epoch 2/50 1387/1387 [==============================] - 18s 13ms/step - loss: 1.4604 - categorical_accuracy: 0.5648 - precision_29: 0.1346 - recall_29: 0.9353 - f1_score: 0.1863 - val_loss: 1.3600 - val_categorical_accuracy: 0.6264 - val_precision_29: 0.1441 - val_recall_29: 0.9482 - val_f1_score: 0.1816 Epoch 3/50 1387/1387 [==============================] - 17s 12ms/step - loss: 1.4025 - categorical_accuracy: 0.5861 - precision_29: 0.1342 - recall_29: 0.9485 - f1_score: 0.1986 - val_loss: 1.3789 - val_categorical_accuracy: 0.6100 - val_precision_29: 0.1353 - val_recall_29: 0.9513 - val_f1_score: 0.1793 Epoch 4/50 1387/1387 [==============================] - 17s 13ms/step - loss: 1.3926 - categorical_accuracy: 0.5894 - precision_29: 0.1327 - recall_29: 0.9515 - f1_score: 0.2117 - val_loss: 1.3101 - val_categorical_accuracy: 0.6504 - val_precision_29: 0.1346 - val_recall_29: 0.9550 - val_f1_score: 0.1777 Epoch 5/50 1387/1387 [==============================] - 18s 13ms/step - loss: 1.3590 - categorical_accuracy: 0.6034 - precision_29: 0.1330 - recall_29: 0.9536 - f1_score: 0.2142 - val_loss: 1.3624 - val_categorical_accuracy: 0.6150 - val_precision_29: 0.1355 - val_recall_29: 0.9558 - val_f1_score: 0.1873 Epoch 6/50 1387/1387 [==============================] - 17s 13ms/step - loss: 1.3550 - categorical_accuracy: 0.6040 - precision_29: 0.1328 - recall_29: 0.9565 - f1_score: 0.2169 - val_loss: 1.3777 - val_categorical_accuracy: 0.6085 - val_precision_29: 0.1364 - val_recall_29: 0.9580 - val_f1_score: 0.1819 Epoch 7/50 1387/1387 [==============================] - 18s 13ms/step - loss: 1.3554 - categorical_accuracy: 0.6009 - precision_29: 0.1320 - recall_29: 0.9546 - f1_score: 0.2302 - val_loss: 1.3181 - val_categorical_accuracy: 0.6130 - val_precision_29: 0.1382 - val_recall_29: 0.9578 - val_f1_score: 0.1895 Epoch 8/50 1387/1387 [==============================] - 17s 13ms/step - loss: 1.3404 - categorical_accuracy: 0.6061 - precision_29: 0.1334 - recall_29: 0.9578 - f1_score: 0.2311 - val_loss: 1.3259 - val_categorical_accuracy: 0.6224 - val_precision_29: 0.1327 - val_recall_29: 0.9585 - val_f1_score: 0.1845 Epoch 9/50 1387/1387 [==============================] - 18s 13ms/step - loss: 1.3254 - categorical_accuracy: 0.6097 - precision_29: 0.1323 - recall_29: 0.9591 - f1_score: 0.2321 - val_loss: 1.3274 - val_categorical_accuracy: 0.6257 - val_precision_29: 0.1371 - val_recall_29: 0.9536 - val_f1_score: 0.1906 Epoch 10/50 1387/1387 [==============================] - 18s 13ms/step - loss: 1.3335 - categorical_accuracy: 0.6123 - precision_29: 0.1336 - recall_29: 0.9577 - f1_score: 0.2334 - val_loss: 1.2943 - val_categorical_accuracy: 0.6315 - val_precision_29: 0.1355 - val_recall_29: 0.9525 - val_f1_score: 0.1905 Epoch 11/50 1387/1387 [==============================] - 18s 13ms/step - loss: 1.3252 - categorical_accuracy: 0.6133 - precision_29: 0.1343 - recall_29: 0.9536 - f1_score: 0.2329 - val_loss: 1.3046 - val_categorical_accuracy: 0.6182 - val_precision_29: 0.1363 - val_recall_29: 0.9631 - val_f1_score: 0.1876 Epoch 12/50 1387/1387 [==============================] - 18s 13ms/step - loss: 1.3214 - categorical_accuracy: 0.6054 - precision_29: 0.1327 - recall_29: 0.9549 - f1_score: 0.2471 - val_loss: 1.3375 - val_categorical_accuracy: 0.6324 - val_precision_29: 0.1335 - val_recall_29: 0.9539 - val_f1_score: 0.1858
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5. warnings.warn(
0 64 30 0.0001 189 48 Epoch 1/50 1363/1363 [==============================] - 50s 36ms/step - loss: 2.3326 - categorical_accuracy: 0.3649 - precision_30: 0.0965 - recall_30: 0.7332 - f1_score: 0.1388 - val_loss: 2.0713 - val_categorical_accuracy: 0.4302 - val_precision_30: 0.1009 - val_recall_30: 0.8006 - val_f1_score: 0.1394 Epoch 2/50 1363/1363 [==============================] - 20s 15ms/step - loss: 1.7957 - categorical_accuracy: 0.4867 - precision_30: 0.1114 - recall_30: 0.8380 - f1_score: 0.1527 - val_loss: 1.8288 - val_categorical_accuracy: 0.4880 - val_precision_30: 0.1146 - val_recall_30: 0.8499 - val_f1_score: 0.1438 Epoch 3/50 1363/1363 [==============================] - 20s 15ms/step - loss: 1.5923 - categorical_accuracy: 0.5435 - precision_30: 0.1235 - recall_30: 0.8798 - f1_score: 0.1570 - val_loss: 1.7159 - val_categorical_accuracy: 0.5086 - val_precision_30: 0.1231 - val_recall_30: 0.8756 - val_f1_score: 0.1499 Epoch 4/50 1363/1363 [==============================] - 20s 15ms/step - loss: 1.4849 - categorical_accuracy: 0.5722 - precision_30: 0.1291 - recall_30: 0.9025 - f1_score: 0.1611 - val_loss: 1.6380 - val_categorical_accuracy: 0.5264 - val_precision_30: 0.1285 - val_recall_30: 0.8923 - val_f1_score: 0.1570 Epoch 5/50 1363/1363 [==============================] - 20s 15ms/step - loss: 1.4230 - categorical_accuracy: 0.5820 - precision_30: 0.1334 - recall_30: 0.9196 - f1_score: 0.1684 - val_loss: 1.6051 - val_categorical_accuracy: 0.5368 - val_precision_30: 0.1295 - val_recall_30: 0.9034 - val_f1_score: 0.1676 Epoch 6/50 1363/1363 [==============================] - 20s 15ms/step - loss: 1.3798 - categorical_accuracy: 0.5865 - precision_30: 0.1340 - recall_30: 0.9265 - f1_score: 0.1743 - val_loss: 1.5842 - val_categorical_accuracy: 0.5383 - val_precision_30: 0.1316 - val_recall_30: 0.9130 - val_f1_score: 0.1731 Epoch 7/50 1363/1363 [==============================] - 20s 15ms/step - loss: 1.3499 - categorical_accuracy: 0.5973 - precision_30: 0.1343 - recall_30: 0.9323 - f1_score: 0.1782 - val_loss: 1.5624 - val_categorical_accuracy: 0.5417 - val_precision_30: 0.1325 - val_recall_30: 0.9223 - val_f1_score: 0.1764 Epoch 8/50 1363/1363 [==============================] - 20s 15ms/step - loss: 1.3228 - categorical_accuracy: 0.6023 - precision_30: 0.1351 - recall_30: 0.9370 - f1_score: 0.1834 - val_loss: 1.5516 - val_categorical_accuracy: 0.5362 - val_precision_30: 0.1315 - val_recall_30: 0.9243 - val_f1_score: 0.1787 Epoch 9/50 1363/1363 [==============================] - 20s 15ms/step - loss: 1.3087 - categorical_accuracy: 0.6113 - precision_30: 0.1344 - recall_30: 0.9405 - f1_score: 0.1842 - val_loss: 1.5447 - val_categorical_accuracy: 0.5377 - val_precision_30: 0.1321 - val_recall_30: 0.9283 - val_f1_score: 0.1801 Epoch 10/50 1363/1363 [==============================] - 20s 15ms/step - loss: 1.2950 - categorical_accuracy: 0.6088 - precision_30: 0.1337 - recall_30: 0.9436 - f1_score: 0.1831 - val_loss: 1.5445 - val_categorical_accuracy: 0.5354 - val_precision_30: 0.1323 - val_recall_30: 0.9310 - val_f1_score: 0.1858 Epoch 11/50 1363/1363 [==============================] - 20s 14ms/step - loss: 1.2837 - categorical_accuracy: 0.6181 - precision_30: 0.1334 - recall_30: 0.9451 - f1_score: 0.1867 - val_loss: 1.5393 - val_categorical_accuracy: 0.5412 - val_precision_30: 0.1314 - val_recall_30: 0.9337 - val_f1_score: 0.1851 Epoch 12/50 1363/1363 [==============================] - 20s 15ms/step - loss: 1.2719 - categorical_accuracy: 0.6146 - precision_30: 0.1343 - recall_30: 0.9478 - f1_score: 0.1885 - val_loss: 1.5393 - val_categorical_accuracy: 0.5400 - val_precision_30: 0.1310 - val_recall_30: 0.9330 - val_f1_score: 0.1862 Epoch 13/50 1363/1363 [==============================] - 20s 15ms/step - loss: 1.2655 - categorical_accuracy: 0.6195 - precision_30: 0.1336 - recall_30: 0.9475 - f1_score: 0.1903 - val_loss: 1.5294 - val_categorical_accuracy: 0.5488 - val_precision_30: 0.1311 - val_recall_30: 0.9363 - val_f1_score: 0.1867 Epoch 14/50 1363/1363 [==============================] - 20s 15ms/step - loss: 1.2559 - categorical_accuracy: 0.6192 - precision_30: 0.1325 - recall_30: 0.9483 - f1_score: 0.1890 - val_loss: 1.5285 - val_categorical_accuracy: 0.5452 - val_precision_30: 0.1306 - val_recall_30: 0.9376 - val_f1_score: 0.1877 Epoch 15/50 1363/1363 [==============================] - 20s 15ms/step - loss: 1.2488 - categorical_accuracy: 0.6213 - precision_30: 0.1328 - recall_30: 0.9510 - f1_score: 0.1930 - val_loss: 1.5496 - val_categorical_accuracy: 0.5260 - val_precision_30: 0.1320 - val_recall_30: 0.9392 - val_f1_score: 0.1892 Epoch 16/50 1363/1363 [==============================] - 20s 15ms/step - loss: 1.2458 - categorical_accuracy: 0.6237 - precision_30: 0.1319 - recall_30: 0.9511 - f1_score: 0.1919 - val_loss: 1.5349 - val_categorical_accuracy: 0.5513 - val_precision_30: 0.1302 - val_recall_30: 0.9364 - val_f1_score: 0.1890 Epoch 17/50 1363/1363 [==============================] - 20s 15ms/step - loss: 1.2389 - categorical_accuracy: 0.6276 - precision_30: 0.1330 - recall_30: 0.9517 - f1_score: 0.1952 - val_loss: 1.5390 - val_categorical_accuracy: 0.5534 - val_precision_30: 0.1286 - val_recall_30: 0.9387 - val_f1_score: 0.1877 Epoch 18/50 1363/1363 [==============================] - 20s 15ms/step - loss: 1.2346 - categorical_accuracy: 0.6252 - precision_30: 0.1323 - recall_30: 0.9520 - f1_score: 0.1953 - val_loss: 1.5447 - val_categorical_accuracy: 0.5512 - val_precision_30: 0.1285 - val_recall_30: 0.9394 - val_f1_score: 0.1876 1 64 30 0.0001 189 48 Epoch 1/50 1367/1367 [==============================] - 21s 15ms/step - loss: 2.3317 - categorical_accuracy: 0.3581 - precision_31: 0.0971 - recall_31: 0.7458 - f1_score: 0.1383 - val_loss: 1.9679 - val_categorical_accuracy: 0.4611 - val_precision_31: 0.1078 - val_recall_31: 0.8001 - val_f1_score: 0.1445 Epoch 2/50 1367/1367 [==============================] - 20s 15ms/step - loss: 1.8171 - categorical_accuracy: 0.4923 - precision_31: 0.1136 - recall_31: 0.8355 - f1_score: 0.1513 - val_loss: 1.7253 - val_categorical_accuracy: 0.5186 - val_precision_31: 0.1174 - val_recall_31: 0.8437 - val_f1_score: 0.1503 Epoch 3/50 1367/1367 [==============================] - 20s 15ms/step - loss: 1.6265 - categorical_accuracy: 0.5362 - precision_31: 0.1214 - recall_31: 0.8782 - f1_score: 0.1562 - val_loss: 1.6051 - val_categorical_accuracy: 0.5418 - val_precision_31: 0.1208 - val_recall_31: 0.8717 - val_f1_score: 0.1544 Epoch 4/50 1367/1367 [==============================] - 20s 15ms/step - loss: 1.5109 - categorical_accuracy: 0.5596 - precision_31: 0.1266 - recall_31: 0.9056 - f1_score: 0.1611 - val_loss: 1.5305 - val_categorical_accuracy: 0.5557 - val_precision_31: 0.1248 - val_recall_31: 0.8909 - val_f1_score: 0.1599 Epoch 5/50 1367/1367 [==============================] - 20s 15ms/step - loss: 1.4461 - categorical_accuracy: 0.5703 - precision_31: 0.1298 - recall_31: 0.9191 - f1_score: 0.1663 - val_loss: 1.4833 - val_categorical_accuracy: 0.5628 - val_precision_31: 0.1276 - val_recall_31: 0.9110 - val_f1_score: 0.1667 Epoch 6/50 1367/1367 [==============================] - 20s 15ms/step - loss: 1.4046 - categorical_accuracy: 0.5804 - precision_31: 0.1301 - recall_31: 0.9267 - f1_score: 0.1700 - val_loss: 1.4595 - val_categorical_accuracy: 0.5572 - val_precision_31: 0.1298 - val_recall_31: 0.9192 - val_f1_score: 0.1726 Epoch 7/50 1367/1367 [==============================] - 31s 23ms/step - loss: 1.3722 - categorical_accuracy: 0.5843 - precision_31: 0.1317 - recall_31: 0.9336 - f1_score: 0.1755 - val_loss: 1.4310 - val_categorical_accuracy: 0.5602 - val_precision_31: 0.1298 - val_recall_31: 0.9254 - val_f1_score: 0.1748 Epoch 8/50 1367/1367 [==============================] - 19s 14ms/step - loss: 1.3502 - categorical_accuracy: 0.5916 - precision_31: 0.1314 - recall_31: 0.9365 - f1_score: 0.1790 - val_loss: 1.4140 - val_categorical_accuracy: 0.5662 - val_precision_31: 0.1310 - val_recall_31: 0.9300 - val_f1_score: 0.1784 Epoch 9/50 1367/1367 [==============================] - 17s 12ms/step - loss: 1.3334 - categorical_accuracy: 0.5944 - precision_31: 0.1323 - recall_31: 0.9385 - f1_score: 0.1815 - val_loss: 1.3888 - val_categorical_accuracy: 0.5843 - val_precision_31: 0.1307 - val_recall_31: 0.9344 - val_f1_score: 0.1798 Epoch 10/50 1367/1367 [==============================] - 17s 12ms/step - loss: 1.3173 - categorical_accuracy: 0.5987 - precision_31: 0.1322 - recall_31: 0.9403 - f1_score: 0.1858 - val_loss: 1.3790 - val_categorical_accuracy: 0.5809 - val_precision_31: 0.1306 - val_recall_31: 0.9385 - val_f1_score: 0.1815 Epoch 11/50 1367/1367 [==============================] - 17s 12ms/step - loss: 1.3057 - categorical_accuracy: 0.6014 - precision_31: 0.1321 - recall_31: 0.9433 - f1_score: 0.1859 - val_loss: 1.3644 - val_categorical_accuracy: 0.5740 - val_precision_31: 0.1314 - val_recall_31: 0.9427 - val_f1_score: 0.1838 Epoch 12/50 1367/1367 [==============================] - 17s 13ms/step - loss: 1.2942 - categorical_accuracy: 0.6017 - precision_31: 0.1321 - recall_31: 0.9436 - f1_score: 0.1896 - val_loss: 1.3543 - val_categorical_accuracy: 0.5813 - val_precision_31: 0.1318 - val_recall_31: 0.9438 - val_f1_score: 0.1840 Epoch 13/50 1367/1367 [==============================] - 17s 12ms/step - loss: 1.2841 - categorical_accuracy: 0.6099 - precision_31: 0.1317 - recall_31: 0.9456 - f1_score: 0.1930 - val_loss: 1.3560 - val_categorical_accuracy: 0.5858 - val_precision_31: 0.1311 - val_recall_31: 0.9458 - val_f1_score: 0.1844 Epoch 14/50 1367/1367 [==============================] - 18s 13ms/step - loss: 1.2773 - categorical_accuracy: 0.6110 - precision_31: 0.1314 - recall_31: 0.9464 - f1_score: 0.1945 - val_loss: 1.3484 - val_categorical_accuracy: 0.5812 - val_precision_31: 0.1316 - val_recall_31: 0.9495 - val_f1_score: 0.1868 Epoch 15/50 1367/1367 [==============================] - 15s 11ms/step - loss: 1.2729 - categorical_accuracy: 0.6137 - precision_31: 0.1320 - recall_31: 0.9472 - f1_score: 0.2008 - val_loss: 1.3371 - val_categorical_accuracy: 0.5833 - val_precision_31: 0.1310 - val_recall_31: 0.9523 - val_f1_score: 0.1879 Epoch 16/50 1367/1367 [==============================] - 15s 11ms/step - loss: 1.2639 - categorical_accuracy: 0.6165 - precision_31: 0.1315 - recall_31: 0.9478 - f1_score: 0.1995 - val_loss: 1.3343 - val_categorical_accuracy: 0.5917 - val_precision_31: 0.1306 - val_recall_31: 0.9527 - val_f1_score: 0.1872 Epoch 17/50 1367/1367 [==============================] - 16s 11ms/step - loss: 1.2586 - categorical_accuracy: 0.6167 - precision_31: 0.1310 - recall_31: 0.9490 - f1_score: 0.2053 - val_loss: 1.3323 - val_categorical_accuracy: 0.5870 - val_precision_31: 0.1314 - val_recall_31: 0.9528 - val_f1_score: 0.1895 Epoch 18/50 1367/1367 [==============================] - 16s 12ms/step - loss: 1.2573 - categorical_accuracy: 0.6162 - precision_31: 0.1307 - recall_31: 0.9508 - f1_score: 0.2051 - val_loss: 1.3213 - val_categorical_accuracy: 0.5933 - val_precision_31: 0.1313 - val_recall_31: 0.9533 - val_f1_score: 0.1895 Epoch 19/50 1367/1367 [==============================] - 16s 11ms/step - loss: 1.2510 - categorical_accuracy: 0.6167 - precision_31: 0.1317 - recall_31: 0.9500 - f1_score: 0.2071 - val_loss: 1.3134 - val_categorical_accuracy: 0.5934 - val_precision_31: 0.1310 - val_recall_31: 0.9564 - val_f1_score: 0.1886 Epoch 20/50 1367/1367 [==============================] - 16s 12ms/step - loss: 1.2469 - categorical_accuracy: 0.6188 - precision_31: 0.1310 - recall_31: 0.9522 - f1_score: 0.2129 - val_loss: 1.3135 - val_categorical_accuracy: 0.5982 - val_precision_31: 0.1302 - val_recall_31: 0.9543 - val_f1_score: 0.1884 Epoch 21/50 1367/1367 [==============================] - 16s 12ms/step - loss: 1.2399 - categorical_accuracy: 0.6258 - precision_31: 0.1305 - recall_31: 0.9514 - f1_score: 0.2117 - val_loss: 1.3117 - val_categorical_accuracy: 0.5979 - val_precision_31: 0.1312 - val_recall_31: 0.9528 - val_f1_score: 0.1903 Epoch 22/50 1367/1367 [==============================] - 16s 12ms/step - loss: 1.2409 - categorical_accuracy: 0.6280 - precision_31: 0.1312 - recall_31: 0.9525 - f1_score: 0.2139 - val_loss: 1.3117 - val_categorical_accuracy: 0.5940 - val_precision_31: 0.1305 - val_recall_31: 0.9550 - val_f1_score: 0.1905 Epoch 23/50 1367/1367 [==============================] - 16s 12ms/step - loss: 1.2379 - categorical_accuracy: 0.6200 - precision_31: 0.1306 - recall_31: 0.9527 - f1_score: 0.2152 - val_loss: 1.3142 - val_categorical_accuracy: 0.5913 - val_precision_31: 0.1312 - val_recall_31: 0.9552 - val_f1_score: 0.1909 Epoch 24/50 1367/1367 [==============================] - 16s 12ms/step - loss: 1.2343 - categorical_accuracy: 0.6280 - precision_31: 0.1304 - recall_31: 0.9528 - f1_score: 0.2166 - val_loss: 1.3052 - val_categorical_accuracy: 0.5944 - val_precision_31: 0.1313 - val_recall_31: 0.9556 - val_f1_score: 0.1914 Epoch 25/50 1367/1367 [==============================] - 16s 12ms/step - loss: 1.2307 - categorical_accuracy: 0.6273 - precision_31: 0.1307 - recall_31: 0.9536 - f1_score: 0.2193 - val_loss: 1.3063 - val_categorical_accuracy: 0.5920 - val_precision_31: 0.1323 - val_recall_31: 0.9558 - val_f1_score: 0.1930 Epoch 26/50 1367/1367 [==============================] - 15s 11ms/step - loss: 1.2307 - categorical_accuracy: 0.6286 - precision_31: 0.1312 - recall_31: 0.9526 - f1_score: 0.2227 - val_loss: 1.3106 - val_categorical_accuracy: 0.5964 - val_precision_31: 0.1311 - val_recall_31: 0.9567 - val_f1_score: 0.1939 Epoch 27/50 1367/1367 [==============================] - 13s 10ms/step - loss: 1.2260 - categorical_accuracy: 0.6305 - precision_31: 0.1308 - recall_31: 0.9527 - f1_score: 0.2211 - val_loss: 1.3113 - val_categorical_accuracy: 0.5963 - val_precision_31: 0.1306 - val_recall_31: 0.9603 - val_f1_score: 0.1910 Epoch 28/50 1367/1367 [==============================] - 14s 10ms/step - loss: 1.2248 - categorical_accuracy: 0.6289 - precision_31: 0.1302 - recall_31: 0.9549 - f1_score: 0.2274 - val_loss: 1.2969 - val_categorical_accuracy: 0.5995 - val_precision_31: 0.1306 - val_recall_31: 0.9595 - val_f1_score: 0.1922 Epoch 29/50 1367/1367 [==============================] - 14s 10ms/step - loss: 1.2199 - categorical_accuracy: 0.6354 - precision_31: 0.1303 - recall_31: 0.9537 - f1_score: 0.2242 - val_loss: 1.2983 - val_categorical_accuracy: 0.5967 - val_precision_31: 0.1309 - val_recall_31: 0.9601 - val_f1_score: 0.1930 2 64 30 0.0001 190 47 Epoch 1/50 1302/1302 [==============================] - 18s 13ms/step - loss: 2.4209 - categorical_accuracy: 0.3506 - precision_32: 0.0975 - recall_32: 0.7196 - f1_score: 0.1352 - val_loss: 2.2467 - val_categorical_accuracy: 0.3611 - val_precision_32: 0.1020 - val_recall_32: 0.7515 - val_f1_score: 0.1417 Epoch 2/50 1302/1302 [==============================] - 16s 13ms/step - loss: 1.8112 - categorical_accuracy: 0.4912 - precision_32: 0.1159 - recall_32: 0.8239 - f1_score: 0.1463 - val_loss: 2.0756 - val_categorical_accuracy: 0.3906 - val_precision_32: 0.1080 - val_recall_32: 0.7863 - val_f1_score: 0.1400 Epoch 3/50 1302/1302 [==============================] - 16s 13ms/step - loss: 1.5951 - categorical_accuracy: 0.5373 - precision_32: 0.1260 - recall_32: 0.8830 - f1_score: 0.1523 - val_loss: 2.0111 - val_categorical_accuracy: 0.4413 - val_precision_32: 0.1143 - val_recall_32: 0.8176 - val_f1_score: 0.1417 Epoch 4/50 1302/1302 [==============================] - 17s 13ms/step - loss: 1.4800 - categorical_accuracy: 0.5692 - precision_32: 0.1318 - recall_32: 0.9100 - f1_score: 0.1586 - val_loss: 2.0010 - val_categorical_accuracy: 0.4370 - val_precision_32: 0.1184 - val_recall_32: 0.8329 - val_f1_score: 0.1450 Epoch 5/50 1302/1302 [==============================] - 17s 13ms/step - loss: 1.4127 - categorical_accuracy: 0.5730 - precision_32: 0.1324 - recall_32: 0.9245 - f1_score: 0.1626 - val_loss: 2.0186 - val_categorical_accuracy: 0.4429 - val_precision_32: 0.1213 - val_recall_32: 0.8506 - val_f1_score: 0.1492 Epoch 6/50 1302/1302 [==============================] - 17s 13ms/step - loss: 1.3680 - categorical_accuracy: 0.5803 - precision_32: 0.1323 - recall_32: 0.9320 - f1_score: 0.1669 - val_loss: 2.0137 - val_categorical_accuracy: 0.4516 - val_precision_32: 0.1199 - val_recall_32: 0.8580 - val_f1_score: 0.1480 Epoch 7/50 1302/1302 [==============================] - 16s 12ms/step - loss: 1.3399 - categorical_accuracy: 0.5843 - precision_32: 0.1325 - recall_32: 0.9379 - f1_score: 0.1715 - val_loss: 2.0300 - val_categorical_accuracy: 0.4485 - val_precision_32: 0.1181 - val_recall_32: 0.8643 - val_f1_score: 0.1468 Epoch 8/50 1302/1302 [==============================] - 17s 13ms/step - loss: 1.3133 - categorical_accuracy: 0.5942 - precision_32: 0.1310 - recall_32: 0.9428 - f1_score: 0.1744 - val_loss: 2.0640 - val_categorical_accuracy: 0.4451 - val_precision_32: 0.1185 - val_recall_32: 0.8731 - val_f1_score: 0.1477 3 64 30 0.0001 190 47 Epoch 1/50 1384/1384 [==============================] - 19s 13ms/step - loss: 2.4260 - categorical_accuracy: 0.3423 - precision_33: 0.0961 - recall_33: 0.7239 - f1_score: 0.1364 - val_loss: 1.9432 - val_categorical_accuracy: 0.4649 - val_precision_33: 0.1142 - val_recall_33: 0.8168 - val_f1_score: 0.1479 Epoch 2/50 1384/1384 [==============================] - 17s 12ms/step - loss: 1.8568 - categorical_accuracy: 0.4728 - precision_33: 0.1167 - recall_33: 0.8387 - f1_score: 0.1550 - val_loss: 1.7105 - val_categorical_accuracy: 0.5213 - val_precision_33: 0.1185 - val_recall_33: 0.8561 - val_f1_score: 0.1485 Epoch 3/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.6528 - categorical_accuracy: 0.5282 - precision_33: 0.1239 - recall_33: 0.8739 - f1_score: 0.1598 - val_loss: 1.5699 - val_categorical_accuracy: 0.5497 - val_precision_33: 0.1238 - val_recall_33: 0.8819 - val_f1_score: 0.1526 Epoch 4/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.5392 - categorical_accuracy: 0.5516 - precision_33: 0.1286 - recall_33: 0.8988 - f1_score: 0.1651 - val_loss: 1.4888 - val_categorical_accuracy: 0.5631 - val_precision_33: 0.1269 - val_recall_33: 0.9024 - val_f1_score: 0.1585 Epoch 5/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.4663 - categorical_accuracy: 0.5638 - precision_33: 0.1303 - recall_33: 0.9143 - f1_score: 0.1692 - val_loss: 1.4410 - val_categorical_accuracy: 0.5802 - val_precision_33: 0.1303 - val_recall_33: 0.9131 - val_f1_score: 0.1646 Epoch 6/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.4208 - categorical_accuracy: 0.5739 - precision_33: 0.1317 - recall_33: 0.9276 - f1_score: 0.1759 - val_loss: 1.4095 - val_categorical_accuracy: 0.5823 - val_precision_33: 0.1284 - val_recall_33: 0.9214 - val_f1_score: 0.1696 Epoch 7/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.3822 - categorical_accuracy: 0.5834 - precision_33: 0.1312 - recall_33: 0.9360 - f1_score: 0.1791 - val_loss: 1.3941 - val_categorical_accuracy: 0.5860 - val_precision_33: 0.1316 - val_recall_33: 0.9263 - val_f1_score: 0.1733 Epoch 8/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.3575 - categorical_accuracy: 0.5898 - precision_33: 0.1328 - recall_33: 0.9418 - f1_score: 0.1849 - val_loss: 1.3920 - val_categorical_accuracy: 0.5715 - val_precision_33: 0.1305 - val_recall_33: 0.9296 - val_f1_score: 0.1769 Epoch 9/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.3401 - categorical_accuracy: 0.5914 - precision_33: 0.1325 - recall_33: 0.9448 - f1_score: 0.1893 - val_loss: 1.3730 - val_categorical_accuracy: 0.5809 - val_precision_33: 0.1305 - val_recall_33: 0.9332 - val_f1_score: 0.1784 Epoch 10/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.3201 - categorical_accuracy: 0.5928 - precision_33: 0.1323 - recall_33: 0.9515 - f1_score: 0.1905 - val_loss: 1.3674 - val_categorical_accuracy: 0.5860 - val_precision_33: 0.1312 - val_recall_33: 0.9347 - val_f1_score: 0.1815 Epoch 11/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.3087 - categorical_accuracy: 0.5954 - precision_33: 0.1326 - recall_33: 0.9528 - f1_score: 0.1955 - val_loss: 1.3527 - val_categorical_accuracy: 0.5924 - val_precision_33: 0.1302 - val_recall_33: 0.9351 - val_f1_score: 0.1805 Epoch 12/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.2966 - categorical_accuracy: 0.6019 - precision_33: 0.1322 - recall_33: 0.9538 - f1_score: 0.1992 - val_loss: 1.3450 - val_categorical_accuracy: 0.5932 - val_precision_33: 0.1313 - val_recall_33: 0.9366 - val_f1_score: 0.1828 Epoch 13/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.2884 - categorical_accuracy: 0.6025 - precision_33: 0.1321 - recall_33: 0.9564 - f1_score: 0.2043 - val_loss: 1.3322 - val_categorical_accuracy: 0.5996 - val_precision_33: 0.1302 - val_recall_33: 0.9368 - val_f1_score: 0.1830 Epoch 14/50 1384/1384 [==============================] - 17s 12ms/step - loss: 1.2768 - categorical_accuracy: 0.6037 - precision_33: 0.1327 - recall_33: 0.9615 - f1_score: 0.2041 - val_loss: 1.3303 - val_categorical_accuracy: 0.6021 - val_precision_33: 0.1306 - val_recall_33: 0.9377 - val_f1_score: 0.1848 Epoch 15/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.2680 - categorical_accuracy: 0.6080 - precision_33: 0.1325 - recall_33: 0.9571 - f1_score: 0.2113 - val_loss: 1.3314 - val_categorical_accuracy: 0.5901 - val_precision_33: 0.1316 - val_recall_33: 0.9391 - val_f1_score: 0.1886 Epoch 16/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.2627 - categorical_accuracy: 0.6090 - precision_33: 0.1331 - recall_33: 0.9642 - f1_score: 0.2100 - val_loss: 1.3232 - val_categorical_accuracy: 0.6014 - val_precision_33: 0.1307 - val_recall_33: 0.9405 - val_f1_score: 0.1867 Epoch 17/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.2599 - categorical_accuracy: 0.6119 - precision_33: 0.1326 - recall_33: 0.9622 - f1_score: 0.2085 - val_loss: 1.3228 - val_categorical_accuracy: 0.5968 - val_precision_33: 0.1303 - val_recall_33: 0.9400 - val_f1_score: 0.1861 Epoch 18/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.2538 - categorical_accuracy: 0.6120 - precision_33: 0.1325 - recall_33: 0.9614 - f1_score: 0.2199 - val_loss: 1.3275 - val_categorical_accuracy: 0.5966 - val_precision_33: 0.1308 - val_recall_33: 0.9418 - val_f1_score: 0.1903 Epoch 19/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.2461 - categorical_accuracy: 0.6175 - precision_33: 0.1330 - recall_33: 0.9641 - f1_score: 0.2174 - val_loss: 1.3261 - val_categorical_accuracy: 0.5967 - val_precision_33: 0.1315 - val_recall_33: 0.9423 - val_f1_score: 0.1923 Epoch 20/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.2447 - categorical_accuracy: 0.6115 - precision_33: 0.1328 - recall_33: 0.9669 - f1_score: 0.2171 - val_loss: 1.3111 - val_categorical_accuracy: 0.6164 - val_precision_33: 0.1309 - val_recall_33: 0.9404 - val_f1_score: 0.1905 Epoch 21/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.2404 - categorical_accuracy: 0.6198 - precision_33: 0.1332 - recall_33: 0.9656 - f1_score: 0.2218 - val_loss: 1.3091 - val_categorical_accuracy: 0.6056 - val_precision_33: 0.1306 - val_recall_33: 0.9415 - val_f1_score: 0.1903 Epoch 22/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.2367 - categorical_accuracy: 0.6176 - precision_33: 0.1327 - recall_33: 0.9677 - f1_score: 0.2222 - val_loss: 1.3068 - val_categorical_accuracy: 0.6101 - val_precision_33: 0.1307 - val_recall_33: 0.9408 - val_f1_score: 0.1919 4 64 30 0.0001 190 47 Epoch 1/50 1384/1384 [==============================] - 18s 12ms/step - loss: 2.4734 - categorical_accuracy: 0.3306 - precision_34: 0.0956 - recall_34: 0.7151 - f1_score: 0.1358 - val_loss: 1.9165 - val_categorical_accuracy: 0.4661 - val_precision_34: 0.1147 - val_recall_34: 0.8133 - val_f1_score: 0.1434 Epoch 2/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.8413 - categorical_accuracy: 0.4699 - precision_34: 0.1168 - recall_34: 0.8482 - f1_score: 0.1568 - val_loss: 1.6380 - val_categorical_accuracy: 0.5421 - val_precision_34: 0.1280 - val_recall_34: 0.8905 - val_f1_score: 0.1544 Epoch 3/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.6363 - categorical_accuracy: 0.5315 - precision_34: 0.1258 - recall_34: 0.8884 - f1_score: 0.1618 - val_loss: 1.5029 - val_categorical_accuracy: 0.5565 - val_precision_34: 0.1350 - val_recall_34: 0.9154 - val_f1_score: 0.1609 Epoch 4/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.5185 - categorical_accuracy: 0.5514 - precision_34: 0.1320 - recall_34: 0.9121 - f1_score: 0.1672 - val_loss: 1.4254 - val_categorical_accuracy: 0.5802 - val_precision_34: 0.1374 - val_recall_34: 0.9280 - val_f1_score: 0.1687 Epoch 5/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.4551 - categorical_accuracy: 0.5689 - precision_34: 0.1343 - recall_34: 0.9250 - f1_score: 0.1743 - val_loss: 1.3807 - val_categorical_accuracy: 0.5853 - val_precision_34: 0.1341 - val_recall_34: 0.9352 - val_f1_score: 0.1664 Epoch 6/50 1384/1384 [==============================] - 26s 19ms/step - loss: 1.4087 - categorical_accuracy: 0.5730 - precision_34: 0.1327 - recall_34: 0.9325 - f1_score: 0.1752 - val_loss: 1.3640 - val_categorical_accuracy: 0.5931 - val_precision_34: 0.1351 - val_recall_34: 0.9409 - val_f1_score: 0.1724 Epoch 7/50 1384/1384 [==============================] - 18s 13ms/step - loss: 1.3772 - categorical_accuracy: 0.5828 - precision_34: 0.1329 - recall_34: 0.9385 - f1_score: 0.1810 - val_loss: 1.3319 - val_categorical_accuracy: 0.5958 - val_precision_34: 0.1345 - val_recall_34: 0.9441 - val_f1_score: 0.1743 Epoch 8/50 1384/1384 [==============================] - 14s 10ms/step - loss: 1.3566 - categorical_accuracy: 0.5842 - precision_34: 0.1332 - recall_34: 0.9398 - f1_score: 0.1851 - val_loss: 1.3160 - val_categorical_accuracy: 0.6076 - val_precision_34: 0.1344 - val_recall_34: 0.9466 - val_f1_score: 0.1746 Epoch 9/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.3366 - categorical_accuracy: 0.5882 - precision_34: 0.1333 - recall_34: 0.9451 - f1_score: 0.1859 - val_loss: 1.3121 - val_categorical_accuracy: 0.5994 - val_precision_34: 0.1345 - val_recall_34: 0.9479 - val_f1_score: 0.1744 Epoch 10/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.3195 - categorical_accuracy: 0.5991 - precision_34: 0.1331 - recall_34: 0.9443 - f1_score: 0.1874 - val_loss: 1.2975 - val_categorical_accuracy: 0.6120 - val_precision_34: 0.1377 - val_recall_34: 0.9492 - val_f1_score: 0.1769 Epoch 11/50 1384/1384 [==============================] - 15s 11ms/step - loss: 1.3131 - categorical_accuracy: 0.5941 - precision_34: 0.1340 - recall_34: 0.9484 - f1_score: 0.1928 - val_loss: 1.2851 - val_categorical_accuracy: 0.6148 - val_precision_34: 0.1346 - val_recall_34: 0.9504 - val_f1_score: 0.1773 Epoch 12/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.3015 - categorical_accuracy: 0.6028 - precision_34: 0.1334 - recall_34: 0.9486 - f1_score: 0.1941 - val_loss: 1.2754 - val_categorical_accuracy: 0.6203 - val_precision_34: 0.1352 - val_recall_34: 0.9512 - val_f1_score: 0.1777 Epoch 13/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.2951 - categorical_accuracy: 0.5959 - precision_34: 0.1337 - recall_34: 0.9507 - f1_score: 0.1992 - val_loss: 1.2808 - val_categorical_accuracy: 0.6139 - val_precision_34: 0.1343 - val_recall_34: 0.9522 - val_f1_score: 0.1797 Epoch 14/50 1384/1384 [==============================] - 15s 11ms/step - loss: 1.2849 - categorical_accuracy: 0.6044 - precision_34: 0.1329 - recall_34: 0.9507 - f1_score: 0.1969 - val_loss: 1.2664 - val_categorical_accuracy: 0.6219 - val_precision_34: 0.1349 - val_recall_34: 0.9523 - val_f1_score: 0.1814 Epoch 15/50 1384/1384 [==============================] - 15s 11ms/step - loss: 1.2821 - categorical_accuracy: 0.6047 - precision_34: 0.1333 - recall_34: 0.9537 - f1_score: 0.2026 - val_loss: 1.2685 - val_categorical_accuracy: 0.6245 - val_precision_34: 0.1345 - val_recall_34: 0.9527 - val_f1_score: 0.1781 Epoch 16/50 1384/1384 [==============================] - 15s 11ms/step - loss: 1.2754 - categorical_accuracy: 0.6070 - precision_34: 0.1331 - recall_34: 0.9540 - f1_score: 0.2051 - val_loss: 1.2576 - val_categorical_accuracy: 0.6214 - val_precision_34: 0.1347 - val_recall_34: 0.9533 - val_f1_score: 0.1786 Epoch 17/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.2677 - categorical_accuracy: 0.6115 - precision_34: 0.1336 - recall_34: 0.9518 - f1_score: 0.2061 - val_loss: 1.2510 - val_categorical_accuracy: 0.6269 - val_precision_34: 0.1335 - val_recall_34: 0.9541 - val_f1_score: 0.1791 0 64 30 0.001 189 48 Epoch 1/50
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:684: UserWarning: The least populated class in y has only 2 members, which is less than n_splits=5. warnings.warn(
1363/1363 [==============================] - 18s 12ms/step - loss: 1.7114 - categorical_accuracy: 0.5182 - precision_35: 0.1278 - recall_35: 0.8797 - f1_score: 0.1621 - val_loss: 1.6433 - val_categorical_accuracy: 0.5247 - val_precision_35: 0.1319 - val_recall_35: 0.9209 - val_f1_score: 0.1660 Epoch 2/50 1363/1363 [==============================] - 16s 12ms/step - loss: 1.4245 - categorical_accuracy: 0.5785 - precision_35: 0.1354 - recall_35: 0.9466 - f1_score: 0.1822 - val_loss: 1.5629 - val_categorical_accuracy: 0.5429 - val_precision_35: 0.1310 - val_recall_35: 0.9384 - val_f1_score: 0.1779 Epoch 3/50 1363/1363 [==============================] - 16s 12ms/step - loss: 1.3806 - categorical_accuracy: 0.5969 - precision_35: 0.1349 - recall_35: 0.9495 - f1_score: 0.1939 - val_loss: 1.5681 - val_categorical_accuracy: 0.5305 - val_precision_35: 0.1293 - val_recall_35: 0.9498 - val_f1_score: 0.1903 Epoch 4/50 1363/1363 [==============================] - 16s 12ms/step - loss: 1.3450 - categorical_accuracy: 0.5993 - precision_35: 0.1330 - recall_35: 0.9582 - f1_score: 0.1972 - val_loss: 1.6460 - val_categorical_accuracy: 0.5249 - val_precision_35: 0.1317 - val_recall_35: 0.9453 - val_f1_score: 0.1981 Epoch 5/50 1363/1363 [==============================] - 16s 12ms/step - loss: 1.3236 - categorical_accuracy: 0.6006 - precision_35: 0.1356 - recall_35: 0.9653 - f1_score: 0.2034 - val_loss: 1.6856 - val_categorical_accuracy: 0.4793 - val_precision_35: 0.1304 - val_recall_35: 0.9497 - val_f1_score: 0.1897 Epoch 6/50 1363/1363 [==============================] - 16s 12ms/step - loss: 1.3279 - categorical_accuracy: 0.6006 - precision_35: 0.1320 - recall_35: 0.9591 - f1_score: 0.2073 - val_loss: 1.5592 - val_categorical_accuracy: 0.5241 - val_precision_35: 0.1300 - val_recall_35: 0.9419 - val_f1_score: 0.1897 Epoch 7/50 1363/1363 [==============================] - 16s 12ms/step - loss: 1.2999 - categorical_accuracy: 0.6135 - precision_35: 0.1336 - recall_35: 0.9622 - f1_score: 0.2107 - val_loss: 1.7586 - val_categorical_accuracy: 0.4796 - val_precision_35: 0.1279 - val_recall_35: 0.9395 - val_f1_score: 0.1885 1 64 30 0.001 189 48 Epoch 1/50 1367/1367 [==============================] - 17s 12ms/step - loss: 1.7607 - categorical_accuracy: 0.5091 - precision_36: 0.1192 - recall_36: 0.8680 - f1_score: 0.1546 - val_loss: 1.5714 - val_categorical_accuracy: 0.4971 - val_precision_36: 0.1360 - val_recall_36: 0.9386 - val_f1_score: 0.1837 Epoch 2/50 1367/1367 [==============================] - 15s 11ms/step - loss: 1.4563 - categorical_accuracy: 0.5720 - precision_36: 0.1307 - recall_36: 0.9420 - f1_score: 0.1804 - val_loss: 1.4834 - val_categorical_accuracy: 0.5649 - val_precision_36: 0.1304 - val_recall_36: 0.9420 - val_f1_score: 0.1913 Epoch 3/50 1367/1367 [==============================] - 15s 11ms/step - loss: 1.4000 - categorical_accuracy: 0.5778 - precision_36: 0.1298 - recall_36: 0.9494 - f1_score: 0.1896 - val_loss: 1.4170 - val_categorical_accuracy: 0.6058 - val_precision_36: 0.1297 - val_recall_36: 0.9544 - val_f1_score: 0.1872 Epoch 4/50 1367/1367 [==============================] - 16s 12ms/step - loss: 1.3853 - categorical_accuracy: 0.5789 - precision_36: 0.1288 - recall_36: 0.9527 - f1_score: 0.1948 - val_loss: 1.3732 - val_categorical_accuracy: 0.5863 - val_precision_36: 0.1315 - val_recall_36: 0.9626 - val_f1_score: 0.1933 Epoch 5/50 1367/1367 [==============================] - 16s 11ms/step - loss: 1.3617 - categorical_accuracy: 0.5855 - precision_36: 0.1293 - recall_36: 0.9547 - f1_score: 0.2025 - val_loss: 1.4197 - val_categorical_accuracy: 0.5869 - val_precision_36: 0.1296 - val_recall_36: 0.9523 - val_f1_score: 0.2000 Epoch 6/50 1367/1367 [==============================] - 15s 11ms/step - loss: 1.3575 - categorical_accuracy: 0.5901 - precision_36: 0.1278 - recall_36: 0.9541 - f1_score: 0.2090 - val_loss: 1.3669 - val_categorical_accuracy: 0.5690 - val_precision_36: 0.1281 - val_recall_36: 0.9706 - val_f1_score: 0.1904 Epoch 7/50 1367/1367 [==============================] - 16s 11ms/step - loss: 1.3299 - categorical_accuracy: 0.5976 - precision_36: 0.1293 - recall_36: 0.9602 - f1_score: 0.2188 - val_loss: 1.4142 - val_categorical_accuracy: 0.6095 - val_precision_36: 0.1304 - val_recall_36: 0.9632 - val_f1_score: 0.1982 Epoch 8/50 1367/1367 [==============================] - 16s 12ms/step - loss: 1.3254 - categorical_accuracy: 0.5957 - precision_36: 0.1302 - recall_36: 0.9596 - f1_score: 0.2168 - val_loss: 1.3974 - val_categorical_accuracy: 0.5936 - val_precision_36: 0.1342 - val_recall_36: 0.9628 - val_f1_score: 0.2034 Epoch 9/50 1367/1367 [==============================] - 16s 12ms/step - loss: 1.2997 - categorical_accuracy: 0.6224 - precision_36: 0.1299 - recall_36: 0.9555 - f1_score: 0.2309 - val_loss: 1.4094 - val_categorical_accuracy: 0.6372 - val_precision_36: 0.1284 - val_recall_36: 0.9320 - val_f1_score: 0.2005 Epoch 10/50 1367/1367 [==============================] - 16s 11ms/step - loss: 1.3182 - categorical_accuracy: 0.6039 - precision_36: 0.1312 - recall_36: 0.9555 - f1_score: 0.2295 - val_loss: 1.3724 - val_categorical_accuracy: 0.6209 - val_precision_36: 0.1343 - val_recall_36: 0.9639 - val_f1_score: 0.2066 Epoch 11/50 1367/1367 [==============================] - 16s 11ms/step - loss: 1.2883 - categorical_accuracy: 0.6187 - precision_36: 0.1324 - recall_36: 0.9583 - f1_score: 0.2335 - val_loss: 1.4336 - val_categorical_accuracy: 0.5655 - val_precision_36: 0.1323 - val_recall_36: 0.9537 - val_f1_score: 0.2054 Epoch 12/50 1367/1367 [==============================] - 15s 11ms/step - loss: 1.2962 - categorical_accuracy: 0.6121 - precision_36: 0.1315 - recall_36: 0.9524 - f1_score: 0.2394 - val_loss: 1.3716 - val_categorical_accuracy: 0.6071 - val_precision_36: 0.1327 - val_recall_36: 0.9647 - val_f1_score: 0.2081 Epoch 13/50 1367/1367 [==============================] - 16s 11ms/step - loss: 1.2769 - categorical_accuracy: 0.6113 - precision_36: 0.1326 - recall_36: 0.9533 - f1_score: 0.2380 - val_loss: 1.3453 - val_categorical_accuracy: 0.5889 - val_precision_36: 0.1353 - val_recall_36: 0.9738 - val_f1_score: 0.2184 Epoch 14/50 1367/1367 [==============================] - 15s 11ms/step - loss: 1.2860 - categorical_accuracy: 0.6126 - precision_36: 0.1315 - recall_36: 0.9519 - f1_score: 0.2405 - val_loss: 1.2934 - val_categorical_accuracy: 0.5847 - val_precision_36: 0.1363 - val_recall_36: 0.9599 - val_f1_score: 0.2141 Epoch 15/50 1367/1367 [==============================] - 15s 11ms/step - loss: 1.2762 - categorical_accuracy: 0.6150 - precision_36: 0.1332 - recall_36: 0.9497 - f1_score: 0.2402 - val_loss: 1.3019 - val_categorical_accuracy: 0.6152 - val_precision_36: 0.1348 - val_recall_36: 0.9585 - val_f1_score: 0.2032 Epoch 16/50 1367/1367 [==============================] - 15s 11ms/step - loss: 1.2719 - categorical_accuracy: 0.6178 - precision_36: 0.1344 - recall_36: 0.9518 - f1_score: 0.2342 - val_loss: 1.3464 - val_categorical_accuracy: 0.6246 - val_precision_36: 0.1328 - val_recall_36: 0.9444 - val_f1_score: 0.2066 2 64 30 0.001 190 47 Epoch 1/50 1302/1302 [==============================] - 17s 13ms/step - loss: 1.6840 - categorical_accuracy: 0.5141 - precision_37: 0.1265 - recall_37: 0.8678 - f1_score: 0.1536 - val_loss: 2.2486 - val_categorical_accuracy: 0.4511 - val_precision_37: 0.1233 - val_recall_37: 0.8543 - val_f1_score: 0.1550 Epoch 2/50 1302/1302 [==============================] - 16s 12ms/step - loss: 1.4174 - categorical_accuracy: 0.5732 - precision_37: 0.1360 - recall_37: 0.9393 - f1_score: 0.1833 - val_loss: 2.1374 - val_categorical_accuracy: 0.4410 - val_precision_37: 0.1240 - val_recall_37: 0.8973 - val_f1_score: 0.1542 Epoch 3/50 1302/1302 [==============================] - 16s 12ms/step - loss: 1.3642 - categorical_accuracy: 0.5881 - precision_37: 0.1320 - recall_37: 0.9463 - f1_score: 0.1919 - val_loss: 2.1997 - val_categorical_accuracy: 0.4409 - val_precision_37: 0.1186 - val_recall_37: 0.9002 - val_f1_score: 0.1538 Epoch 4/50 1302/1302 [==============================] - 16s 13ms/step - loss: 1.3305 - categorical_accuracy: 0.5940 - precision_37: 0.1331 - recall_37: 0.9507 - f1_score: 0.1918 - val_loss: 2.3175 - val_categorical_accuracy: 0.4254 - val_precision_37: 0.1219 - val_recall_37: 0.8991 - val_f1_score: 0.1598 Epoch 5/50 1302/1302 [==============================] - 16s 12ms/step - loss: 1.3201 - categorical_accuracy: 0.5976 - precision_37: 0.1338 - recall_37: 0.9516 - f1_score: 0.2057 - val_loss: 2.3671 - val_categorical_accuracy: 0.4713 - val_precision_37: 0.1243 - val_recall_37: 0.8974 - val_f1_score: 0.1638 Epoch 6/50 1302/1302 [==============================] - 15s 12ms/step - loss: 1.3023 - categorical_accuracy: 0.5954 - precision_37: 0.1349 - recall_37: 0.9540 - f1_score: 0.2085 - val_loss: 2.2928 - val_categorical_accuracy: 0.4594 - val_precision_37: 0.1190 - val_recall_37: 0.8874 - val_f1_score: 0.1567 Epoch 7/50 1302/1302 [==============================] - 16s 12ms/step - loss: 1.3077 - categorical_accuracy: 0.5990 - precision_37: 0.1325 - recall_37: 0.9542 - f1_score: 0.2101 - val_loss: 2.3921 - val_categorical_accuracy: 0.4606 - val_precision_37: 0.1213 - val_recall_37: 0.8895 - val_f1_score: 0.1592 Epoch 8/50 1302/1302 [==============================] - 16s 12ms/step - loss: 1.2983 - categorical_accuracy: 0.6022 - precision_37: 0.1345 - recall_37: 0.9525 - f1_score: 0.2180 - val_loss: 2.2415 - val_categorical_accuracy: 0.4373 - val_precision_37: 0.1251 - val_recall_37: 0.9318 - val_f1_score: 0.1640 Epoch 9/50 1302/1302 [==============================] - 16s 12ms/step - loss: 1.2868 - categorical_accuracy: 0.6041 - precision_37: 0.1319 - recall_37: 0.9502 - f1_score: 0.2093 - val_loss: 2.2906 - val_categorical_accuracy: 0.4675 - val_precision_37: 0.1203 - val_recall_37: 0.8710 - val_f1_score: 0.1616 Epoch 10/50 1302/1302 [==============================] - 16s 12ms/step - loss: 1.2607 - categorical_accuracy: 0.6109 - precision_37: 0.1343 - recall_37: 0.9555 - f1_score: 0.2196 - val_loss: 2.2299 - val_categorical_accuracy: 0.4832 - val_precision_37: 0.1256 - val_recall_37: 0.9204 - val_f1_score: 0.1646 Epoch 11/50 1302/1302 [==============================] - 16s 12ms/step - loss: 1.2702 - categorical_accuracy: 0.6030 - precision_37: 0.1363 - recall_37: 0.9540 - f1_score: 0.2095 - val_loss: 2.3051 - val_categorical_accuracy: 0.4572 - val_precision_37: 0.1194 - val_recall_37: 0.8740 - val_f1_score: 0.1593 Epoch 12/50 1302/1302 [==============================] - 16s 12ms/step - loss: 1.2556 - categorical_accuracy: 0.6116 - precision_37: 0.1355 - recall_37: 0.9536 - f1_score: 0.2209 - val_loss: 2.3873 - val_categorical_accuracy: 0.4476 - val_precision_37: 0.1255 - val_recall_37: 0.8736 - val_f1_score: 0.1689 Epoch 13/50 1302/1302 [==============================] - 16s 12ms/step - loss: 1.2603 - categorical_accuracy: 0.6153 - precision_37: 0.1381 - recall_37: 0.9508 - f1_score: 0.2212 - val_loss: 2.2618 - val_categorical_accuracy: 0.4592 - val_precision_37: 0.1251 - val_recall_37: 0.8753 - val_f1_score: 0.1663 Epoch 14/50 1302/1302 [==============================] - 15s 12ms/step - loss: 1.2412 - categorical_accuracy: 0.6148 - precision_37: 0.1380 - recall_37: 0.9515 - f1_score: 0.2280 - val_loss: 2.3480 - val_categorical_accuracy: 0.4617 - val_precision_37: 0.1248 - val_recall_37: 0.8763 - val_f1_score: 0.1666 Epoch 15/50 1302/1302 [==============================] - 16s 12ms/step - loss: 1.2341 - categorical_accuracy: 0.6231 - precision_37: 0.1384 - recall_37: 0.9487 - f1_score: 0.2308 - val_loss: 2.3455 - val_categorical_accuracy: 0.4867 - val_precision_37: 0.1263 - val_recall_37: 0.8741 - val_f1_score: 0.1712 Epoch 16/50 1302/1302 [==============================] - 15s 12ms/step - loss: 1.2387 - categorical_accuracy: 0.6230 - precision_37: 0.1403 - recall_37: 0.9500 - f1_score: 0.2405 - val_loss: 2.3370 - val_categorical_accuracy: 0.4435 - val_precision_37: 0.1237 - val_recall_37: 0.8531 - val_f1_score: 0.1655 Epoch 17/50 1302/1302 [==============================] - 16s 12ms/step - loss: 1.2310 - categorical_accuracy: 0.6237 - precision_37: 0.1396 - recall_37: 0.9487 - f1_score: 0.2484 - val_loss: 2.3450 - val_categorical_accuracy: 0.4671 - val_precision_37: 0.1306 - val_recall_37: 0.9191 - val_f1_score: 0.1711 Epoch 18/50 1302/1302 [==============================] - 15s 12ms/step - loss: 1.2349 - categorical_accuracy: 0.6249 - precision_37: 0.1395 - recall_37: 0.9479 - f1_score: 0.2416 - val_loss: 2.4740 - val_categorical_accuracy: 0.4554 - val_precision_37: 0.1263 - val_recall_37: 0.8915 - val_f1_score: 0.1666 3 64 30 0.001 190 47 Epoch 1/50 1384/1384 [==============================] - 18s 12ms/step - loss: 1.7511 - categorical_accuracy: 0.5004 - precision_38: 0.1221 - recall_38: 0.8683 - f1_score: 0.1601 - val_loss: 1.4534 - val_categorical_accuracy: 0.5907 - val_precision_38: 0.1351 - val_recall_38: 0.9322 - val_f1_score: 0.1781 Epoch 2/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.4545 - categorical_accuracy: 0.5494 - precision_38: 0.1316 - recall_38: 0.9430 - f1_score: 0.1855 - val_loss: 1.4483 - val_categorical_accuracy: 0.5527 - val_precision_38: 0.1308 - val_recall_38: 0.9345 - val_f1_score: 0.1846 Epoch 3/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.4105 - categorical_accuracy: 0.5741 - precision_38: 0.1322 - recall_38: 0.9491 - f1_score: 0.1975 - val_loss: 1.3494 - val_categorical_accuracy: 0.5934 - val_precision_38: 0.1258 - val_recall_38: 0.9387 - val_f1_score: 0.1840 Epoch 4/50 1384/1384 [==============================] - 15s 11ms/step - loss: 1.3690 - categorical_accuracy: 0.5803 - precision_38: 0.1324 - recall_38: 0.9569 - f1_score: 0.1986 - val_loss: 1.3934 - val_categorical_accuracy: 0.5660 - val_precision_38: 0.1351 - val_recall_38: 0.9493 - val_f1_score: 0.1933 Epoch 5/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.3622 - categorical_accuracy: 0.5855 - precision_38: 0.1309 - recall_38: 0.9615 - f1_score: 0.2028 - val_loss: 1.3424 - val_categorical_accuracy: 0.6108 - val_precision_38: 0.1315 - val_recall_38: 0.9426 - val_f1_score: 0.1994 Epoch 6/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.3332 - categorical_accuracy: 0.5895 - precision_38: 0.1333 - recall_38: 0.9647 - f1_score: 0.2099 - val_loss: 1.4191 - val_categorical_accuracy: 0.6054 - val_precision_38: 0.1297 - val_recall_38: 0.9454 - val_f1_score: 0.2029 Epoch 7/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.3246 - categorical_accuracy: 0.6005 - precision_38: 0.1337 - recall_38: 0.9613 - f1_score: 0.2184 - val_loss: 1.3188 - val_categorical_accuracy: 0.6084 - val_precision_38: 0.1293 - val_recall_38: 0.9472 - val_f1_score: 0.1986 Epoch 8/50 1384/1384 [==============================] - 15s 11ms/step - loss: 1.3192 - categorical_accuracy: 0.5958 - precision_38: 0.1330 - recall_38: 0.9644 - f1_score: 0.2171 - val_loss: 1.3251 - val_categorical_accuracy: 0.6025 - val_precision_38: 0.1338 - val_recall_38: 0.9422 - val_f1_score: 0.2010 Epoch 9/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.2957 - categorical_accuracy: 0.6015 - precision_38: 0.1346 - recall_38: 0.9629 - f1_score: 0.2279 - val_loss: 1.4146 - val_categorical_accuracy: 0.5337 - val_precision_38: 0.1312 - val_recall_38: 0.9490 - val_f1_score: 0.1912 4 64 30 0.001 190 47 Epoch 1/50 1384/1384 [==============================] - 17s 12ms/step - loss: 1.7417 - categorical_accuracy: 0.5049 - precision_39: 0.1263 - recall_39: 0.8767 - f1_score: 0.1644 - val_loss: 1.4306 - val_categorical_accuracy: 0.5654 - val_precision_39: 0.1431 - val_recall_39: 0.9452 - val_f1_score: 0.1772 Epoch 2/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.4604 - categorical_accuracy: 0.5586 - precision_39: 0.1329 - recall_39: 0.9386 - f1_score: 0.1870 - val_loss: 1.4800 - val_categorical_accuracy: 0.5374 - val_precision_39: 0.1331 - val_recall_39: 0.9450 - val_f1_score: 0.1705 Epoch 3/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.4016 - categorical_accuracy: 0.5708 - precision_39: 0.1318 - recall_39: 0.9464 - f1_score: 0.1962 - val_loss: 1.3745 - val_categorical_accuracy: 0.6129 - val_precision_39: 0.1397 - val_recall_39: 0.9601 - val_f1_score: 0.1782 Epoch 4/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.3847 - categorical_accuracy: 0.5757 - precision_39: 0.1321 - recall_39: 0.9499 - f1_score: 0.2012 - val_loss: 1.5143 - val_categorical_accuracy: 0.6151 - val_precision_39: 0.1290 - val_recall_39: 0.9388 - val_f1_score: 0.1704 Epoch 5/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.3641 - categorical_accuracy: 0.5911 - precision_39: 0.1319 - recall_39: 0.9479 - f1_score: 0.2117 - val_loss: 1.4278 - val_categorical_accuracy: 0.6083 - val_precision_39: 0.1374 - val_recall_39: 0.9456 - val_f1_score: 0.1860 Epoch 6/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.3610 - categorical_accuracy: 0.5881 - precision_39: 0.1313 - recall_39: 0.9526 - f1_score: 0.2112 - val_loss: 1.2696 - val_categorical_accuracy: 0.6520 - val_precision_39: 0.1342 - val_recall_39: 0.9582 - val_f1_score: 0.1866 Epoch 7/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.3436 - categorical_accuracy: 0.5924 - precision_39: 0.1314 - recall_39: 0.9538 - f1_score: 0.2186 - val_loss: 1.3433 - val_categorical_accuracy: 0.6122 - val_precision_39: 0.1328 - val_recall_39: 0.9545 - val_f1_score: 0.1831 Epoch 8/50 1384/1384 [==============================] - 16s 12ms/step - loss: 1.3210 - categorical_accuracy: 0.5971 - precision_39: 0.1325 - recall_39: 0.9563 - f1_score: 0.2279 - val_loss: 1.2966 - val_categorical_accuracy: 0.6456 - val_precision_39: 0.1311 - val_recall_39: 0.9570 - val_f1_score: 0.1798 Epoch 9/50 1384/1384 [==============================] - 17s 12ms/step - loss: 1.3303 - categorical_accuracy: 0.5913 - precision_39: 0.1323 - recall_39: 0.9543 - f1_score: 0.2216 - val_loss: 1.3124 - val_categorical_accuracy: 0.6165 - val_precision_39: 0.1339 - val_recall_39: 0.9586 - val_f1_score: 0.1914 Epoch 10/50 1384/1384 [==============================] - 17s 12ms/step - loss: 1.3142 - categorical_accuracy: 0.5983 - precision_39: 0.1325 - recall_39: 0.9571 - f1_score: 0.2354 - val_loss: 1.3469 - val_categorical_accuracy: 0.5891 - val_precision_39: 0.1324 - val_recall_39: 0.9580 - val_f1_score: 0.1762 Epoch 11/50 1384/1384 [==============================] - 14s 10ms/step - loss: 1.3073 - categorical_accuracy: 0.6093 - precision_39: 0.1336 - recall_39: 0.9517 - f1_score: 0.2460 - val_loss: 1.2693 - val_categorical_accuracy: 0.6227 - val_precision_39: 0.1396 - val_recall_39: 0.9508 - val_f1_score: 0.1924 Epoch 12/50 1384/1384 [==============================] - 15s 11ms/step - loss: 1.2949 - categorical_accuracy: 0.6027 - precision_39: 0.1357 - recall_39: 0.9557 - f1_score: 0.2427 - val_loss: 1.2850 - val_categorical_accuracy: 0.6334 - val_precision_39: 0.1347 - val_recall_39: 0.9547 - val_f1_score: 0.1926 Epoch 13/50 1384/1384 [==============================] - 14s 10ms/step - loss: 1.2970 - categorical_accuracy: 0.6000 - precision_39: 0.1342 - recall_39: 0.9537 - f1_score: 0.2409 - val_loss: 1.2687 - val_categorical_accuracy: 0.6406 - val_precision_39: 0.1351 - val_recall_39: 0.9555 - val_f1_score: 0.1933 Epoch 14/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.2933 - categorical_accuracy: 0.6116 - precision_39: 0.1358 - recall_39: 0.9495 - f1_score: 0.2483 - val_loss: 1.2182 - val_categorical_accuracy: 0.6512 - val_precision_39: 0.1351 - val_recall_39: 0.9554 - val_f1_score: 0.1859 Epoch 15/50 1384/1384 [==============================] - 14s 10ms/step - loss: 1.2847 - categorical_accuracy: 0.6119 - precision_39: 0.1351 - recall_39: 0.9543 - f1_score: 0.2505 - val_loss: 1.2407 - val_categorical_accuracy: 0.6581 - val_precision_39: 0.1392 - val_recall_39: 0.9555 - val_f1_score: 0.1913 Epoch 16/50 1384/1384 [==============================] - 14s 10ms/step - loss: 1.2734 - categorical_accuracy: 0.6242 - precision_39: 0.1361 - recall_39: 0.9511 - f1_score: 0.2622 - val_loss: 1.3422 - val_categorical_accuracy: 0.5958 - val_precision_39: 0.1379 - val_recall_39: 0.9574 - val_f1_score: 0.2137 Epoch 17/50 1384/1384 [==============================] - 13s 10ms/step - loss: 1.2728 - categorical_accuracy: 0.6139 - precision_39: 0.1375 - recall_39: 0.9495 - f1_score: 0.2626 - val_loss: 1.2775 - val_categorical_accuracy: 0.6400 - val_precision_39: 0.1413 - val_recall_39: 0.9537 - val_f1_score: 0.2038 Epoch 18/50 1384/1384 [==============================] - 16s 11ms/step - loss: 1.2752 - categorical_accuracy: 0.6119 - precision_39: 0.1381 - recall_39: 0.9529 - f1_score: 0.2668 - val_loss: 1.2264 - val_categorical_accuracy: 0.6587 - val_precision_39: 0.1371 - val_recall_39: 0.9485 - val_f1_score: 0.1976 Epoch 19/50 1384/1384 [==============================] - 15s 11ms/step - loss: 1.2693 - categorical_accuracy: 0.6174 - precision_39: 0.1367 - recall_39: 0.9475 - f1_score: 0.2551 - val_loss: 1.2093 - val_categorical_accuracy: 0.6545 - val_precision_39: 0.1406 - val_recall_39: 0.9566 - val_f1_score: 0.2102
| val_accuracy | val_precision | val_recall | val_f1_score | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| mean | std | mean | std | mean | std | mean | std | |||
| batch_size | seq_length | learning_rate | ||||||||
| 32 | 15 | 0.0001 | 0.572148 | 0.073204 | 0.408438 | 0.063195 | 0.396537 | 0.027559 | 0.378847 | 0.040708 |
| 0.0010 | 0.577516 | 0.068717 | 0.476084 | 0.080048 | 0.440360 | 0.029731 | 0.424277 | 0.039733 | ||
| 30 | 0.0001 | 0.561612 | 0.062351 | 0.474269 | 0.072688 | 0.435835 | 0.039429 | 0.412130 | 0.050721 | |
| 0.0010 | 0.551434 | 0.087456 | 0.495603 | 0.113382 | 0.441186 | 0.050074 | 0.419919 | 0.076330 | ||
| 64 | 15 | 0.0001 | 0.565692 | 0.058694 | 0.420889 | 0.055004 | 0.407307 | 0.015851 | 0.388302 | 0.020709 |
| 0.0010 | 0.573203 | 0.061259 | 0.452327 | 0.072715 | 0.424280 | 0.022099 | 0.412525 | 0.040651 | ||
| 30 | 0.0001 | 0.556785 | 0.073003 | 0.461286 | 0.098666 | 0.414262 | 0.054505 | 0.402358 | 0.067456 | |
| 0.0010 | 0.560334 | 0.051917 | 0.488388 | 0.100515 | 0.446361 | 0.017267 | 0.426248 | 0.044673 | ||
Wall time: 5h 4min 32s
rep_df = pd.DataFrame(linear_param_fit.report)
rep_df['val_accuracy'] = rep_df.class_rep_val.apply(pd.Series)['accuracy']
rep_df['val_precision'] = rep_df.class_rep_val.apply(pd.Series)['macro avg'].apply(pd.Series)['precision']
rep_df['val_recall'] = rep_df.class_rep_val.apply(pd.Series)['macro avg'].apply(pd.Series)['recall']
rep_df['val_f1_score'] = rep_df.class_rep_val.apply(pd.Series)['macro avg'].apply(pd.Series)['f1-score']
rep_df.drop(['class_rep_val', 'class_rep_test'], axis=1, inplace=True)
fdict = {'val_accuracy': ['mean', 'std'], 'val_precision': ['mean', 'std'],
'val_recall': ['mean', 'std'], 'val_f1_score': ['mean', 'std']}
rep_df_stat = rep_df.groupby(['batch_size', 'seq_length', 'learning_rate']).agg(fdict)
rep_df_stat.reset_index(inplace=True)
fig, ax = plt.subplots()
plt.style.use('ggplot')
sns.scatterplot(data=rep_df, x='learning_rate', y='val_f1_score',
hue=rep_df['batch_size'], palette=['red', 'blue'], alpha=0.25,
style=rep_df['seq_length'], ax=ax)
sns.scatterplot(data=rep_df_stat, x='learning_rate', y=('val_f1_score', 'mean'),
hue='batch_size', palette=['red', 'blue'],
style='seq_length', ax=ax, s=100)
#rep_df_stat.plot.scatter(x='learning_rate', y=('val_f1_score', 'mean'), ax=ax, s=100, marker='x')
h, l = ax.get_legend_handles_labels()
ax.legend(handles=h[:-6], labels=l[:-6], loc='lower center')
plt.show()
%%time
train_df, test_df, val_df = dset.split(real=False, simul=True, drawn=True, test_size=0.2, val_size=0.1)
tf.keras.backend.clear_session()
tf.compat.v1.reset_default_graph()
linear_model = kmodel('LinearModel', LinearModel, flist0, 'class', categories, 64, 30,
train_df, val_df, test_df, 'D:/datatmp', reset_ts=False, class_bal=True)
linear_model.compile_and_fit(max_epochs=50, patience=5, lr=0.001)
linear_model.save()
Instances Train: 690 Test: 192 Validation: 77
Model: "linear_model_40"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten_40 (Flatten) (64, 480) 0
layer_normalization_40 (Lay (64, 480) 960
erNormalization)
dense_40 (Dense) (64, 17) 8177
=================================================================
Total params: 9,137
Trainable params: 9,137
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/50
7095/7095 [==============================] - 159s 22ms/step - loss: 1.2580 - categorical_accuracy: 0.6088 - precision_40: 0.1208 - recall_40: 0.9416 - f1_score: 0.1702 - val_loss: 1.1321 - val_categorical_accuracy: 0.6807 - val_precision_40: 0.1263 - val_recall_40: 0.9643 - val_f1_score: 0.1799
Epoch 2/50
7095/7095 [==============================] - 80s 11ms/step - loss: 0.9494 - categorical_accuracy: 0.6529 - precision_40: 0.1242 - recall_40: 0.9767 - f1_score: 0.1753 - val_loss: 0.9508 - val_categorical_accuracy: 0.7281 - val_precision_40: 0.1235 - val_recall_40: 0.9849 - val_f1_score: 0.1768
Epoch 3/50
7095/7095 [==============================] - 79s 11ms/step - loss: 0.9184 - categorical_accuracy: 0.6583 - precision_40: 0.1255 - recall_40: 0.9786 - f1_score: 0.1774 - val_loss: 0.8233 - val_categorical_accuracy: 0.7722 - val_precision_40: 0.1268 - val_recall_40: 0.9830 - val_f1_score: 0.1844
Epoch 4/50
7095/7095 [==============================] - 82s 12ms/step - loss: 0.8818 - categorical_accuracy: 0.6649 - precision_40: 0.1265 - recall_40: 0.9776 - f1_score: 0.1795 - val_loss: 0.8927 - val_categorical_accuracy: 0.7243 - val_precision_40: 0.1287 - val_recall_40: 0.9811 - val_f1_score: 0.1733
Epoch 5/50
7095/7095 [==============================] - 80s 11ms/step - loss: 0.8411 - categorical_accuracy: 0.6761 - precision_40: 0.1287 - recall_40: 0.9765 - f1_score: 0.1769 - val_loss: 0.8744 - val_categorical_accuracy: 0.7430 - val_precision_40: 0.1295 - val_recall_40: 0.9860 - val_f1_score: 0.1875
Epoch 6/50
7095/7095 [==============================] - 80s 11ms/step - loss: 0.8184 - categorical_accuracy: 0.6828 - precision_40: 0.1318 - recall_40: 0.9759 - f1_score: 0.1845 - val_loss: 0.9267 - val_categorical_accuracy: 0.6771 - val_precision_40: 0.1365 - val_recall_40: 0.9839 - val_f1_score: 0.1924
Epoch 7/50
7095/7095 [==============================] - 79s 11ms/step - loss: 0.8197 - categorical_accuracy: 0.6754 - precision_40: 0.1355 - recall_40: 0.9681 - f1_score: 0.1887 - val_loss: 0.8432 - val_categorical_accuracy: 0.7406 - val_precision_40: 0.1380 - val_recall_40: 0.9696 - val_f1_score: 0.1965
Epoch 8/50
7095/7095 [==============================] - 80s 11ms/step - loss: 0.8027 - categorical_accuracy: 0.6811 - precision_40: 0.1382 - recall_40: 0.9681 - f1_score: 0.1945 - val_loss: 0.8929 - val_categorical_accuracy: 0.7484 - val_precision_40: 0.1397 - val_recall_40: 0.9652 - val_f1_score: 0.1976
Epoch 9/50
7095/7095 [==============================] - 79s 11ms/step - loss: 0.7931 - categorical_accuracy: 0.6888 - precision_40: 0.1397 - recall_40: 0.9631 - f1_score: 0.1949 - val_loss: 0.9717 - val_categorical_accuracy: 0.6976 - val_precision_40: 0.1394 - val_recall_40: 0.9748 - val_f1_score: 0.1950
Epoch 10/50
7095/7095 [==============================] - 80s 11ms/step - loss: 0.7881 - categorical_accuracy: 0.6855 - precision_40: 0.1424 - recall_40: 0.9621 - f1_score: 0.1983 - val_loss: 0.8069 - val_categorical_accuracy: 0.7675 - val_precision_40: 0.1461 - val_recall_40: 0.9778 - val_f1_score: 0.2046
Epoch 11/50
7095/7095 [==============================] - 79s 11ms/step - loss: 0.7803 - categorical_accuracy: 0.6866 - precision_40: 0.1451 - recall_40: 0.9570 - f1_score: 0.2029 - val_loss: 0.8575 - val_categorical_accuracy: 0.7324 - val_precision_40: 0.1466 - val_recall_40: 0.9665 - val_f1_score: 0.2046
Epoch 12/50
7095/7095 [==============================] - 82s 11ms/step - loss: 0.7732 - categorical_accuracy: 0.6912 - precision_40: 0.1473 - recall_40: 0.9504 - f1_score: 0.2060 - val_loss: 0.7982 - val_categorical_accuracy: 0.7495 - val_precision_40: 0.1516 - val_recall_40: 0.9666 - val_f1_score: 0.2174
Epoch 13/50
7095/7095 [==============================] - 82s 12ms/step - loss: 0.7773 - categorical_accuracy: 0.6887 - precision_40: 0.1490 - recall_40: 0.9450 - f1_score: 0.2072 - val_loss: 0.9464 - val_categorical_accuracy: 0.7171 - val_precision_40: 0.1469 - val_recall_40: 0.9648 - val_f1_score: 0.2060
Epoch 14/50
7095/7095 [==============================] - 83s 12ms/step - loss: 0.7667 - categorical_accuracy: 0.6884 - precision_40: 0.1504 - recall_40: 0.9412 - f1_score: 0.2077 - val_loss: 0.8710 - val_categorical_accuracy: 0.7220 - val_precision_40: 0.1499 - val_recall_40: 0.9486 - val_f1_score: 0.2050
Epoch 15/50
7095/7095 [==============================] - 78s 11ms/step - loss: 0.7675 - categorical_accuracy: 0.6889 - precision_40: 0.1536 - recall_40: 0.9304 - f1_score: 0.2126 - val_loss: 0.9404 - val_categorical_accuracy: 0.7119 - val_precision_40: 0.1520 - val_recall_40: 0.9390 - val_f1_score: 0.2103
Epoch 16/50
7095/7095 [==============================] - 77s 11ms/step - loss: 0.7677 - categorical_accuracy: 0.6911 - precision_40: 0.1544 - recall_40: 0.9210 - f1_score: 0.2119 - val_loss: 0.9921 - val_categorical_accuracy: 0.7138 - val_precision_40: 0.1545 - val_recall_40: 0.9446 - val_f1_score: 0.2095
Epoch 17/50
7095/7095 [==============================] - 79s 11ms/step - loss: 0.7605 - categorical_accuracy: 0.6903 - precision_40: 0.1558 - recall_40: 0.9136 - f1_score: 0.2138 - val_loss: 0.8364 - val_categorical_accuracy: 0.7325 - val_precision_40: 0.1573 - val_recall_40: 0.9410 - val_f1_score: 0.2150
Val Classification Report
precision recall f1-score support
0 0.26 0.12 0.16 880
1 0.81 0.82 0.81 4200
2 0.95 1.00 0.97 360
3 0.80 0.14 0.24 6030
5 0.99 0.90 0.94 14062
6 0.98 0.99 0.99 5100
7 0.29 0.59 0.39 87
8 0.81 0.95 0.88 1148
101 0.76 0.74 0.75 6000
102 0.57 0.98 0.72 60
105 0.75 0.95 0.84 2038
106 0.73 0.91 0.81 2040
107 0.17 0.68 0.28 1210
108 0.29 0.59 0.39 1792
accuracy 0.75 45007
macro avg 0.65 0.74 0.65 45007
weighted avg 0.82 0.75 0.75 45007
Test Classification Report
precision recall f1-score support
0 0.24 0.46 0.32 2424
1 0.68 0.84 0.75 9129
2 0.61 0.96 0.74 1080
3 0.79 0.23 0.35 13674
5 0.92 0.83 0.87 35281
6 0.99 0.97 0.98 12867
7 0.02 0.01 0.02 217
8 0.82 0.95 0.88 2163
101 0.70 0.40 0.51 19782
102 0.26 0.96 0.41 180
105 0.78 0.95 0.85 5199
106 0.67 0.93 0.78 5193
107 0.13 0.47 0.20 3099
108 0.29 0.60 0.39 4557
accuracy 0.69 114845
macro avg 0.56 0.68 0.58 114845
weighted avg 0.77 0.69 0.69 114845
All Data Classification Report
precision recall f1-score support
0 0.40 0.46 0.43 14210
1 0.72 0.88 0.79 48336
2 0.75 0.98 0.85 5760
3 0.76 0.19 0.30 68980
5 0.96 0.85 0.90 176014
6 0.97 0.97 0.97 64434
7 0.66 0.72 0.69 1801
8 0.76 0.96 0.85 10059
101 0.69 0.53 0.60 86657
102 0.35 0.97 0.52 960
105 0.83 0.95 0.88 25926
106 0.69 0.92 0.79 25866
107 0.31 0.66 0.42 34072
108 0.31 0.61 0.41 23961
accuracy 0.72 587036
macro avg 0.66 0.76 0.67 587036
weighted avg 0.78 0.72 0.72 587036
model weights saved in linear_model_40_batch_size_64-seq_length_30-2022-09-08T16_28_24.h5 Wall time: 26min 30s
|
|
class DNNModel(tf.keras.Model):
def __init__(self):
super().__init__()
self.flatten = layers.Flatten()
self.norm = layers.LayerNormalization()
self.dense0 = layers.Dense(units=256, activation='relu')
self.drop0 = layers.Dropout(0.1, seed=200560)
self.dense1 = layers.Dense(units=128, activation='relu')
self.drop1 = layers.Dropout(0.1, seed=200560)
self.dense2 = layers.Dense(units=64, activation='relu')
self.drop2 = layers.Dropout(0.1, seed=200560)
self.dense3 = layers.Dense(units=17, activation='relu')
self.softmax = layers.Softmax()
def call(self, inputs, training=False):
x = self.flatten(inputs)
x = self.norm(x)
x = self.dense0(x)
if training:
x = self.drop0(x, training=training)
x = self.dense1(x)
if training:
x = self.drop1(x, training=training)
x = self.dense2(x)
if training:
x = self.drop2(x, training=training)
x = self.dense3(x)
return self.softmax(x)
%%time
train_df, test_df, val_df = dset.split(real=True, simul=True, drawn=True, test_size=0.2, val_size=0.1)
tf.keras.backend.clear_session()
tf.compat.v1.reset_default_graph()
dnn_model = kmodel('DNNModel', DNNModel, flist0, 'class', categories, 64, 30,
train_df, val_df, test_df, 'D:/datatmp', reset_ts=False, class_bal=True)
dnn_model.compile_and_fit(max_epochs=50, patience=5, lr=0.0001)
dnn_model.save()
Instances Train: 1425 Test: 397 Validation: 159
Model: "dnn_model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
flatten (Flatten) (64, 480) 0
layer_normalization (LayerN (64, 480) 960
ormalization)
dense (Dense) (64, 256) 123136
dropout (Dropout) (64, 256) 0
dense_1 (Dense) (64, 128) 32896
dropout_1 (Dropout) (64, 128) 0
dense_2 (Dense) (64, 64) 8256
dropout_2 (Dropout) (64, 64) 0
dense_3 (Dense) (64, 17) 1105
softmax (Softmax) (64, 17) 0
=================================================================
Total params: 166,353
Trainable params: 166,353
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/50
9819/9819 [==============================] - 122s 12ms/step - loss: 1.3475 - categorical_accuracy: 0.5779 - precision: 0.8182 - recall: 0.4472 - f1_score: 0.4466 - val_loss: 0.9733 - val_categorical_accuracy: 0.6828 - val_precision: 0.8273 - val_recall: 0.5979 - val_f1_score: 0.5242
Epoch 2/50
9819/9819 [==============================] - 102s 10ms/step - loss: 0.7546 - categorical_accuracy: 0.7031 - precision: 0.8311 - recall: 0.6391 - f1_score: 0.5945 - val_loss: 0.7486 - val_categorical_accuracy: 0.7485 - val_precision: 0.8556 - val_recall: 0.7000 - val_f1_score: 0.5966
Epoch 3/50
9819/9819 [==============================] - 95s 10ms/step - loss: 0.5795 - categorical_accuracy: 0.7492 - precision: 0.8525 - recall: 0.7041 - f1_score: 0.6410 - val_loss: 0.7118 - val_categorical_accuracy: 0.7595 - val_precision: 0.8687 - val_recall: 0.7113 - val_f1_score: 0.6026
Epoch 4/50
9819/9819 [==============================] - 100s 10ms/step - loss: 0.5139 - categorical_accuracy: 0.7724 - precision: 0.8683 - recall: 0.7363 - f1_score: 0.6603 - val_loss: 0.6200 - val_categorical_accuracy: 0.7947 - val_precision: 0.8909 - val_recall: 0.7662 - val_f1_score: 0.6279
Epoch 5/50
9819/9819 [==============================] - 96s 10ms/step - loss: 0.4886 - categorical_accuracy: 0.7886 - precision: 0.8794 - recall: 0.7589 - f1_score: 0.6746 - val_loss: 0.6038 - val_categorical_accuracy: 0.8016 - val_precision: 0.8938 - val_recall: 0.7745 - val_f1_score: 0.6279
Epoch 6/50
9819/9819 [==============================] - 99s 10ms/step - loss: 0.4588 - categorical_accuracy: 0.7983 - precision: 0.8859 - recall: 0.7729 - f1_score: 0.6825 - val_loss: 0.5691 - val_categorical_accuracy: 0.8053 - val_precision: 0.8988 - val_recall: 0.7817 - val_f1_score: 0.6301
Epoch 7/50
9819/9819 [==============================] - 101s 10ms/step - loss: 0.4438 - categorical_accuracy: 0.8082 - precision: 0.8935 - recall: 0.7872 - f1_score: 0.6910 - val_loss: 0.5825 - val_categorical_accuracy: 0.7996 - val_precision: 0.8939 - val_recall: 0.7760 - val_f1_score: 0.6454
Epoch 8/50
9819/9819 [==============================] - 99s 10ms/step - loss: 0.4214 - categorical_accuracy: 0.8154 - precision: 0.8996 - recall: 0.7970 - f1_score: 0.6989 - val_loss: 0.5398 - val_categorical_accuracy: 0.8133 - val_precision: 0.9045 - val_recall: 0.7989 - val_f1_score: 0.6464
Epoch 9/50
9819/9819 [==============================] - 100s 10ms/step - loss: 0.4015 - categorical_accuracy: 0.8207 - precision: 0.9033 - recall: 0.8050 - f1_score: 0.7059 - val_loss: 0.5195 - val_categorical_accuracy: 0.8216 - val_precision: 0.9138 - val_recall: 0.8104 - val_f1_score: 0.6527
Epoch 10/50
9819/9819 [==============================] - 137s 14ms/step - loss: 0.3963 - categorical_accuracy: 0.8283 - precision: 0.9103 - recall: 0.8139 - f1_score: 0.7132 - val_loss: 0.5231 - val_categorical_accuracy: 0.8204 - val_precision: 0.9088 - val_recall: 0.8118 - val_f1_score: 0.6540
Epoch 11/50
9819/9819 [==============================] - 110s 11ms/step - loss: 0.3853 - categorical_accuracy: 0.8314 - precision: 0.9125 - recall: 0.8190 - f1_score: 0.7153 - val_loss: 0.5043 - val_categorical_accuracy: 0.8237 - val_precision: 0.9142 - val_recall: 0.8128 - val_f1_score: 0.6636
Epoch 12/50
9819/9819 [==============================] - 98s 10ms/step - loss: 0.3792 - categorical_accuracy: 0.8361 - precision: 0.9168 - recall: 0.8248 - f1_score: 0.7220 - val_loss: 0.4975 - val_categorical_accuracy: 0.8294 - val_precision: 0.9212 - val_recall: 0.8168 - val_f1_score: 0.6641
Epoch 13/50
9819/9819 [==============================] - 97s 10ms/step - loss: 0.3672 - categorical_accuracy: 0.8397 - precision: 0.9200 - recall: 0.8296 - f1_score: 0.7241 - val_loss: 0.5210 - val_categorical_accuracy: 0.8217 - val_precision: 0.9091 - val_recall: 0.8131 - val_f1_score: 0.6603
Epoch 14/50
9819/9819 [==============================] - 94s 10ms/step - loss: 0.3544 - categorical_accuracy: 0.8441 - precision: 0.9240 - recall: 0.8352 - f1_score: 0.7322 - val_loss: 0.4848 - val_categorical_accuracy: 0.8347 - val_precision: 0.9224 - val_recall: 0.8284 - val_f1_score: 0.6614
Epoch 15/50
9819/9819 [==============================] - 99s 10ms/step - loss: 0.3541 - categorical_accuracy: 0.8457 - precision: 0.9254 - recall: 0.8377 - f1_score: 0.7313 - val_loss: 0.4851 - val_categorical_accuracy: 0.8329 - val_precision: 0.9232 - val_recall: 0.8231 - val_f1_score: 0.6686
Epoch 16/50
9819/9819 [==============================] - 94s 10ms/step - loss: 0.3447 - categorical_accuracy: 0.8489 - precision: 0.9280 - recall: 0.8409 - f1_score: 0.7344 - val_loss: 0.4555 - val_categorical_accuracy: 0.8400 - val_precision: 0.9279 - val_recall: 0.8348 - val_f1_score: 0.6799
Epoch 17/50
9819/9819 [==============================] - 98s 10ms/step - loss: 0.3445 - categorical_accuracy: 0.8503 - precision: 0.9291 - recall: 0.8432 - f1_score: 0.7347 - val_loss: 0.4761 - val_categorical_accuracy: 0.8352 - val_precision: 0.9234 - val_recall: 0.8299 - val_f1_score: 0.6731
Epoch 18/50
9819/9819 [==============================] - 98s 10ms/step - loss: 0.3422 - categorical_accuracy: 0.8525 - precision: 0.9311 - recall: 0.8458 - f1_score: 0.7381 - val_loss: 0.4777 - val_categorical_accuracy: 0.8359 - val_precision: 0.9223 - val_recall: 0.8320 - val_f1_score: 0.6802
Epoch 19/50
9819/9819 [==============================] - 97s 10ms/step - loss: 0.3349 - categorical_accuracy: 0.8543 - precision: 0.9328 - recall: 0.8483 - f1_score: 0.7421 - val_loss: 0.4727 - val_categorical_accuracy: 0.8339 - val_precision: 0.9223 - val_recall: 0.8271 - val_f1_score: 0.6650
Epoch 20/50
9819/9819 [==============================] - 97s 10ms/step - loss: 0.3309 - categorical_accuracy: 0.8562 - precision: 0.9347 - recall: 0.8503 - f1_score: 0.7389 - val_loss: 0.4718 - val_categorical_accuracy: 0.8374 - val_precision: 0.9255 - val_recall: 0.8329 - val_f1_score: 0.6821
Epoch 21/50
9819/9819 [==============================] - 97s 10ms/step - loss: 0.3273 - categorical_accuracy: 0.8576 - precision: 0.9361 - recall: 0.8518 - f1_score: 0.7439 - val_loss: 0.4648 - val_categorical_accuracy: 0.8395 - val_precision: 0.9282 - val_recall: 0.8340 - val_f1_score: 0.6730
Epoch 22/50
9819/9819 [==============================] - 98s 10ms/step - loss: 0.3193 - categorical_accuracy: 0.8599 - precision: 0.9383 - recall: 0.8550 - f1_score: 0.7467 - val_loss: 0.4601 - val_categorical_accuracy: 0.8397 - val_precision: 0.9255 - val_recall: 0.8369 - val_f1_score: 0.6772
Epoch 23/50
9819/9819 [==============================] - 97s 10ms/step - loss: 0.3163 - categorical_accuracy: 0.8614 - precision: 0.9396 - recall: 0.8568 - f1_score: 0.7491 - val_loss: 0.4521 - val_categorical_accuracy: 0.8444 - val_precision: 0.9308 - val_recall: 0.8421 - val_f1_score: 0.6812
Epoch 24/50
9819/9819 [==============================] - 96s 10ms/step - loss: 0.3142 - categorical_accuracy: 0.8626 - precision: 0.9407 - recall: 0.8582 - f1_score: 0.7510 - val_loss: 0.4462 - val_categorical_accuracy: 0.8458 - val_precision: 0.9333 - val_recall: 0.8426 - val_f1_score: 0.6821
Epoch 25/50
9819/9819 [==============================] - 100s 10ms/step - loss: 0.3125 - categorical_accuracy: 0.8634 - precision: 0.9413 - recall: 0.8591 - f1_score: 0.7525 - val_loss: 0.4521 - val_categorical_accuracy: 0.8428 - val_precision: 0.9299 - val_recall: 0.8393 - val_f1_score: 0.6823
Epoch 26/50
9819/9819 [==============================] - 97s 10ms/step - loss: 0.3130 - categorical_accuracy: 0.8648 - precision: 0.9428 - recall: 0.8605 - f1_score: 0.7507 - val_loss: 0.4513 - val_categorical_accuracy: 0.8447 - val_precision: 0.9303 - val_recall: 0.8420 - val_f1_score: 0.6844
Epoch 27/50
9819/9819 [==============================] - 97s 10ms/step - loss: 0.3050 - categorical_accuracy: 0.8662 - precision: 0.9441 - recall: 0.8625 - f1_score: 0.7574 - val_loss: 0.4413 - val_categorical_accuracy: 0.8477 - val_precision: 0.9341 - val_recall: 0.8456 - val_f1_score: 0.6935
Epoch 28/50
9819/9819 [==============================] - 97s 10ms/step - loss: 0.3060 - categorical_accuracy: 0.8667 - precision: 0.9446 - recall: 0.8631 - f1_score: 0.7570 - val_loss: 0.4538 - val_categorical_accuracy: 0.8407 - val_precision: 0.9280 - val_recall: 0.8368 - val_f1_score: 0.6817
Epoch 29/50
9819/9819 [==============================] - 98s 10ms/step - loss: 0.2991 - categorical_accuracy: 0.8683 - precision: 0.9463 - recall: 0.8644 - f1_score: 0.7593 - val_loss: 0.4472 - val_categorical_accuracy: 0.8456 - val_precision: 0.9316 - val_recall: 0.8436 - val_f1_score: 0.6766
Epoch 30/50
9819/9819 [==============================] - 98s 10ms/step - loss: 0.3004 - categorical_accuracy: 0.8691 - precision: 0.9471 - recall: 0.8658 - f1_score: 0.7585 - val_loss: 0.4547 - val_categorical_accuracy: 0.8486 - val_precision: 0.9349 - val_recall: 0.8467 - val_f1_score: 0.6939
Epoch 31/50
9819/9819 [==============================] - 97s 10ms/step - loss: 0.3024 - categorical_accuracy: 0.8702 - precision: 0.9480 - recall: 0.8671 - f1_score: 0.7590 - val_loss: 0.4303 - val_categorical_accuracy: 0.8501 - val_precision: 0.9361 - val_recall: 0.8481 - val_f1_score: 0.6865
Epoch 32/50
9819/9819 [==============================] - 97s 10ms/step - loss: 0.2951 - categorical_accuracy: 0.8709 - precision: 0.9488 - recall: 0.8675 - f1_score: 0.7616 - val_loss: 0.4346 - val_categorical_accuracy: 0.8467 - val_precision: 0.9334 - val_recall: 0.8443 - val_f1_score: 0.6794
Epoch 33/50
9819/9819 [==============================] - 97s 10ms/step - loss: 0.2936 - categorical_accuracy: 0.8717 - precision: 0.9496 - recall: 0.8685 - f1_score: 0.7634 - val_loss: 0.4584 - val_categorical_accuracy: 0.8431 - val_precision: 0.9287 - val_recall: 0.8412 - val_f1_score: 0.6878
Epoch 34/50
9819/9819 [==============================] - 97s 10ms/step - loss: 0.2900 - categorical_accuracy: 0.8731 - precision: 0.9508 - recall: 0.8702 - f1_score: 0.7622 - val_loss: 0.4592 - val_categorical_accuracy: 0.8397 - val_precision: 0.9262 - val_recall: 0.8375 - val_f1_score: 0.6827
Epoch 35/50
9819/9819 [==============================] - 97s 10ms/step - loss: 0.2871 - categorical_accuracy: 0.8730 - precision: 0.9509 - recall: 0.8702 - f1_score: 0.7631 - val_loss: 0.4421 - val_categorical_accuracy: 0.8541 - val_precision: 0.9416 - val_recall: 0.8522 - val_f1_score: 0.6888
Val Classification Report
precision recall f1-score support
0 0.68 0.91 0.78 12978
1 0.78 0.99 0.87 3780
2 1.00 0.97 0.98 360
3 0.99 0.95 0.97 4650
4 0.80 0.91 0.85 2539
5 0.99 0.98 0.99 14123
6 0.00 0.00 0.00 5400
7 0.00 0.00 0.00 14
8 0.90 0.99 0.95 748
101 0.95 0.83 0.89 7435
102 0.97 0.81 0.89 81
105 0.88 0.99 0.93 2004
106 0.84 1.00 0.91 2160
107 0.55 0.72 0.62 635
108 0.99 0.95 0.97 1772
accuracy 0.85 58679
macro avg 0.75 0.80 0.77 58679
weighted avg 0.79 0.85 0.81 58679
Test Classification Report
precision recall f1-score support
0 0.69 0.88 0.77 33789
1 0.83 0.98 0.90 9399
2 0.90 1.00 0.95 1097
3 0.99 0.93 0.96 15400
4 0.75 0.97 0.84 6246
5 1.00 0.97 0.98 35960
6 0.00 0.00 0.00 12914
7 0.51 1.00 0.68 87
8 0.81 0.99 0.89 1746
101 0.92 0.86 0.89 18727
102 0.71 0.72 0.72 452
105 0.82 0.98 0.89 5151
106 0.83 0.99 0.90 5173
107 0.91 0.99 0.95 11580
108 0.99 0.92 0.95 4974
accuracy 0.86 162695
macro avg 0.78 0.88 0.82 162695
weighted avg 0.80 0.86 0.83 162695
All Data Classification Report
precision recall f1-score support
0 0.70 0.92 0.80 164820
1 0.87 0.98 0.92 48436
2 0.93 1.00 0.96 6042
3 0.99 0.98 0.99 77554
4 0.84 0.98 0.91 31211
5 1.00 0.98 0.99 176186
6 0.00 0.00 0.00 64648
7 0.66 0.99 0.79 1815
8 0.90 0.99 0.94 10059
101 0.95 0.89 0.92 87937
102 0.78 0.82 0.80 2339
105 0.89 0.99 0.94 31150
106 0.84 1.00 0.91 25955
107 0.91 0.96 0.93 38338
108 0.99 0.95 0.97 23961
accuracy 0.88 790451
macro avg 0.82 0.90 0.85 790451
weighted avg 0.82 0.88 0.84 790451
model weights saved in dnn_model_batch_size_64-seq_length_30-2022-09-08T17_33_24.h5 Wall time: 1h 2min 36s
class RNNModel(tf.keras.Model):
def __init__(self):
super().__init__()
self.lstm = layers.LSTM(128, return_sequences=False, dropout=0.15, name='lstm')
self.dense1 = layers.Dense(17, activation='relu', name='dense1')
self.softmax = layers.Softmax(name='softmax')
return
def call(self, inputs, training=False):
x = self.lstm(inputs, training=training)
x = self.dense1(x)
return self.softmax(x)
%%time
train_df, test_df, val_df = dset.split(real=True, simul=True, drawn=True, test_size=0.2, val_size=0.1)
tf.keras.backend.clear_session()
tf.compat.v1.reset_default_graph()
rnn_model = kmodel('RNNModel', RNNModel, flist0, 'class', categories, 64, 30,
train_df, val_df, test_df, 'D:/datatmp', reset_ts=False, class_bal=True)
rnn_model.compile_and_fit(max_epochs=50, patience=5, lr=0.001)
rnn_model.save()
Instances Train: 1425 Test: 397 Validation: 159
Model: "rnn_model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm (LSTM) (64, 128) 74240
dense1 (Dense) (64, 17) 2193
softmax (Softmax) (64, 17) 0
=================================================================
Total params: 76,433
Trainable params: 76,433
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/50
9819/9819 [==============================] - 114s 11ms/step - loss: 1.3668 - categorical_accuracy: 0.6274 - precision: 0.7926 - recall: 0.5006 - f1_score: 0.4497 - val_loss: 0.5997 - val_categorical_accuracy: 0.7978 - val_precision: 0.8589 - val_recall: 0.7180 - val_f1_score: 0.5834
Epoch 2/50
9819/9819 [==============================] - 99s 10ms/step - loss: 0.6433 - categorical_accuracy: 0.8318 - precision: 0.8749 - recall: 0.7839 - f1_score: 0.6502 - val_loss: 0.4087 - val_categorical_accuracy: 0.8727 - val_precision: 0.9008 - val_recall: 0.8453 - val_f1_score: 0.6625
Epoch 3/50
9819/9819 [==============================] - 100s 10ms/step - loss: 0.4909 - categorical_accuracy: 0.8868 - precision: 0.9102 - recall: 0.8634 - f1_score: 0.7056 - val_loss: 0.3839 - val_categorical_accuracy: 0.8710 - val_precision: 0.8949 - val_recall: 0.8451 - val_f1_score: 0.6703
Epoch 4/50
9819/9819 [==============================] - 105s 11ms/step - loss: 0.4168 - categorical_accuracy: 0.9084 - precision: 0.9249 - recall: 0.8949 - f1_score: 0.7287 - val_loss: 0.3131 - val_categorical_accuracy: 0.8936 - val_precision: 0.9084 - val_recall: 0.8779 - val_f1_score: 0.7042
Epoch 5/50
9819/9819 [==============================] - 106s 11ms/step - loss: 0.3817 - categorical_accuracy: 0.9222 - precision: 0.9353 - recall: 0.9122 - f1_score: 0.7437 - val_loss: 0.2156 - val_categorical_accuracy: 0.9257 - val_precision: 0.9342 - val_recall: 0.9202 - val_f1_score: 0.7318
Epoch 6/50
9819/9819 [==============================] - 104s 11ms/step - loss: 0.3526 - categorical_accuracy: 0.9324 - precision: 0.9424 - recall: 0.9243 - f1_score: 0.7536 - val_loss: 0.2890 - val_categorical_accuracy: 0.9034 - val_precision: 0.9195 - val_recall: 0.8912 - val_f1_score: 0.6519
Epoch 7/50
9819/9819 [==============================] - 104s 11ms/step - loss: 0.3336 - categorical_accuracy: 0.9397 - precision: 0.9481 - recall: 0.9327 - f1_score: 0.7598 - val_loss: 0.2302 - val_categorical_accuracy: 0.9238 - val_precision: 0.9285 - val_recall: 0.9205 - val_f1_score: 0.7337
Epoch 8/50
9819/9819 [==============================] - 103s 10ms/step - loss: 0.3138 - categorical_accuracy: 0.9455 - precision: 0.9521 - recall: 0.9405 - f1_score: 0.7681 - val_loss: 0.2043 - val_categorical_accuracy: 0.9295 - val_precision: 0.9348 - val_recall: 0.9261 - val_f1_score: 0.7369
Epoch 9/50
9819/9819 [==============================] - 103s 10ms/step - loss: 0.3114 - categorical_accuracy: 0.9479 - precision: 0.9550 - recall: 0.9435 - f1_score: 0.7701 - val_loss: 0.2375 - val_categorical_accuracy: 0.9289 - val_precision: 0.9341 - val_recall: 0.9224 - val_f1_score: 0.7263
Epoch 10/50
9819/9819 [==============================] - 103s 11ms/step - loss: 0.2971 - categorical_accuracy: 0.9545 - precision: 0.9599 - recall: 0.9511 - f1_score: 0.7736 - val_loss: 0.2579 - val_categorical_accuracy: 0.9187 - val_precision: 0.9252 - val_recall: 0.9139 - val_f1_score: 0.7094
Epoch 11/50
9819/9819 [==============================] - 106s 11ms/step - loss: 0.2903 - categorical_accuracy: 0.9546 - precision: 0.9597 - recall: 0.9521 - f1_score: 0.7765 - val_loss: 0.2144 - val_categorical_accuracy: 0.9266 - val_precision: 0.9335 - val_recall: 0.9217 - val_f1_score: 0.7292
Epoch 12/50
9819/9819 [==============================] - 104s 11ms/step - loss: 0.2793 - categorical_accuracy: 0.9589 - precision: 0.9638 - recall: 0.9565 - f1_score: 0.7815 - val_loss: 0.2043 - val_categorical_accuracy: 0.9341 - val_precision: 0.9384 - val_recall: 0.9308 - val_f1_score: 0.7482
Epoch 13/50
9819/9819 [==============================] - 102s 10ms/step - loss: 0.2800 - categorical_accuracy: 0.9611 - precision: 0.9653 - recall: 0.9588 - f1_score: 0.7819 - val_loss: 0.2424 - val_categorical_accuracy: 0.9295 - val_precision: 0.9332 - val_recall: 0.9263 - val_f1_score: 0.7423
Epoch 14/50
9819/9819 [==============================] - 103s 10ms/step - loss: 0.2714 - categorical_accuracy: 0.9626 - precision: 0.9669 - recall: 0.9608 - f1_score: 0.7835 - val_loss: 0.2162 - val_categorical_accuracy: 0.9368 - val_precision: 0.9404 - val_recall: 0.9339 - val_f1_score: 0.7481
Epoch 15/50
9819/9819 [==============================] - 103s 10ms/step - loss: 0.2688 - categorical_accuracy: 0.9643 - precision: 0.9686 - recall: 0.9630 - f1_score: 0.7846 - val_loss: 0.2437 - val_categorical_accuracy: 0.9325 - val_precision: 0.9354 - val_recall: 0.9307 - val_f1_score: 0.7500
Epoch 16/50
9819/9819 [==============================] - 103s 11ms/step - loss: 0.2657 - categorical_accuracy: 0.9649 - precision: 0.9690 - recall: 0.9635 - f1_score: 0.7851 - val_loss: 0.2073 - val_categorical_accuracy: 0.9371 - val_precision: 0.9400 - val_recall: 0.9351 - val_f1_score: 0.7446
Epoch 17/50
9819/9819 [==============================] - 105s 11ms/step - loss: 0.2643 - categorical_accuracy: 0.9661 - precision: 0.9707 - recall: 0.9646 - f1_score: 0.7874 - val_loss: 0.2357 - val_categorical_accuracy: 0.9350 - val_precision: 0.9376 - val_recall: 0.9331 - val_f1_score: 0.7498
Epoch 18/50
9819/9819 [==============================] - 103s 11ms/step - loss: 0.2574 - categorical_accuracy: 0.9670 - precision: 0.9709 - recall: 0.9658 - f1_score: 0.7884 - val_loss: 0.2125 - val_categorical_accuracy: 0.9379 - val_precision: 0.9405 - val_recall: 0.9363 - val_f1_score: 0.7474
Epoch 19/50
9819/9819 [==============================] - 103s 10ms/step - loss: 0.2578 - categorical_accuracy: 0.9683 - precision: 0.9720 - recall: 0.9674 - f1_score: 0.7889 - val_loss: 0.2048 - val_categorical_accuracy: 0.9391 - val_precision: 0.9411 - val_recall: 0.9377 - val_f1_score: 0.7556
Epoch 20/50
9819/9819 [==============================] - 103s 10ms/step - loss: 0.2566 - categorical_accuracy: 0.9688 - precision: 0.9725 - recall: 0.9678 - f1_score: 0.7895 - val_loss: 0.1768 - val_categorical_accuracy: 0.9472 - val_precision: 0.9486 - val_recall: 0.9457 - val_f1_score: 0.7536
Epoch 21/50
9819/9819 [==============================] - 102s 10ms/step - loss: 0.2529 - categorical_accuracy: 0.9698 - precision: 0.9734 - recall: 0.9688 - f1_score: 0.7906 - val_loss: 0.2140 - val_categorical_accuracy: 0.9404 - val_precision: 0.9422 - val_recall: 0.9391 - val_f1_score: 0.7526
Epoch 22/50
9819/9819 [==============================] - 104s 11ms/step - loss: 0.2542 - categorical_accuracy: 0.9711 - precision: 0.9748 - recall: 0.9703 - f1_score: 0.7918 - val_loss: 0.2228 - val_categorical_accuracy: 0.9395 - val_precision: 0.9423 - val_recall: 0.9377 - val_f1_score: 0.7505
Epoch 23/50
9819/9819 [==============================] - 102s 10ms/step - loss: 0.2515 - categorical_accuracy: 0.9720 - precision: 0.9756 - recall: 0.9713 - f1_score: 0.7920 - val_loss: 0.2329 - val_categorical_accuracy: 0.9356 - val_precision: 0.9380 - val_recall: 0.9337 - val_f1_score: 0.7490
Epoch 24/50
9819/9819 [==============================] - 104s 11ms/step - loss: 0.2490 - categorical_accuracy: 0.9724 - precision: 0.9759 - recall: 0.9717 - f1_score: 0.7921 - val_loss: 0.2098 - val_categorical_accuracy: 0.9448 - val_precision: 0.9471 - val_recall: 0.9437 - val_f1_score: 0.7560
Epoch 25/50
9819/9819 [==============================] - 102s 10ms/step - loss: 0.2532 - categorical_accuracy: 0.9732 - precision: 0.9766 - recall: 0.9724 - f1_score: 0.7921 - val_loss: 0.2465 - val_categorical_accuracy: 0.9370 - val_precision: 0.9384 - val_recall: 0.9355 - val_f1_score: 0.7556
Epoch 26/50
9819/9819 [==============================] - 102s 10ms/step - loss: 0.2451 - categorical_accuracy: 0.9730 - precision: 0.9764 - recall: 0.9724 - f1_score: 0.7925 - val_loss: 0.2174 - val_categorical_accuracy: 0.9425 - val_precision: 0.9448 - val_recall: 0.9412 - val_f1_score: 0.7562
Epoch 27/50
9819/9819 [==============================] - 105s 11ms/step - loss: 0.2487 - categorical_accuracy: 0.9728 - precision: 0.9763 - recall: 0.9723 - f1_score: 0.7936 - val_loss: 0.2401 - val_categorical_accuracy: 0.9384 - val_precision: 0.9404 - val_recall: 0.9373 - val_f1_score: 0.7556
Epoch 28/50
9819/9819 [==============================] - 102s 10ms/step - loss: 0.2447 - categorical_accuracy: 0.9742 - precision: 0.9776 - recall: 0.9735 - f1_score: 0.7952 - val_loss: 0.2352 - val_categorical_accuracy: 0.9388 - val_precision: 0.9408 - val_recall: 0.9371 - val_f1_score: 0.7515
Epoch 29/50
9819/9819 [==============================] - 102s 10ms/step - loss: 0.2448 - categorical_accuracy: 0.9740 - precision: 0.9774 - recall: 0.9734 - f1_score: 0.7930 - val_loss: 0.2131 - val_categorical_accuracy: 0.9457 - val_precision: 0.9470 - val_recall: 0.9448 - val_f1_score: 0.7581
Epoch 30/50
9819/9819 [==============================] - 102s 10ms/step - loss: 0.2441 - categorical_accuracy: 0.9746 - precision: 0.9779 - recall: 0.9741 - f1_score: 0.7940 - val_loss: 0.2012 - val_categorical_accuracy: 0.9495 - val_precision: 0.9509 - val_recall: 0.9484 - val_f1_score: 0.7585
Epoch 31/50
9819/9819 [==============================] - 102s 10ms/step - loss: 0.2453 - categorical_accuracy: 0.9749 - precision: 0.9782 - recall: 0.9744 - f1_score: 0.7958 - val_loss: 0.2544 - val_categorical_accuracy: 0.9400 - val_precision: 0.9417 - val_recall: 0.9383 - val_f1_score: 0.7575
Epoch 32/50
9819/9819 [==============================] - 104s 11ms/step - loss: 0.2367 - categorical_accuracy: 0.9762 - precision: 0.9795 - recall: 0.9757 - f1_score: 0.7985 - val_loss: 0.2474 - val_categorical_accuracy: 0.9416 - val_precision: 0.9439 - val_recall: 0.9402 - val_f1_score: 0.7578
Epoch 33/50
9819/9819 [==============================] - 105s 11ms/step - loss: 0.2434 - categorical_accuracy: 0.9760 - precision: 0.9794 - recall: 0.9755 - f1_score: 0.7957 - val_loss: 0.2404 - val_categorical_accuracy: 0.9419 - val_precision: 0.9435 - val_recall: 0.9409 - val_f1_score: 0.7555
Epoch 34/50
9819/9819 [==============================] - 103s 11ms/step - loss: 0.2384 - categorical_accuracy: 0.9763 - precision: 0.9797 - recall: 0.9756 - f1_score: 0.7987 - val_loss: 0.2101 - val_categorical_accuracy: 0.9476 - val_precision: 0.9494 - val_recall: 0.9468 - val_f1_score: 0.7571
Epoch 35/50
9819/9819 [==============================] - 104s 11ms/step - loss: 0.2426 - categorical_accuracy: 0.9766 - precision: 0.9799 - recall: 0.9762 - f1_score: 0.7966 - val_loss: 0.2265 - val_categorical_accuracy: 0.9470 - val_precision: 0.9483 - val_recall: 0.9460 - val_f1_score: 0.7610
Epoch 36/50
9819/9819 [==============================] - 105s 11ms/step - loss: 0.2476 - categorical_accuracy: 0.9766 - precision: 0.9800 - recall: 0.9762 - f1_score: 0.7981 - val_loss: 0.2243 - val_categorical_accuracy: 0.9423 - val_precision: 0.9437 - val_recall: 0.9412 - val_f1_score: 0.7525
Epoch 37/50
9819/9819 [==============================] - 107s 11ms/step - loss: 0.2374 - categorical_accuracy: 0.9779 - precision: 0.9812 - recall: 0.9775 - f1_score: 0.7994 - val_loss: 0.2419 - val_categorical_accuracy: 0.9421 - val_precision: 0.9434 - val_recall: 0.9414 - val_f1_score: 0.7553
Epoch 38/50
9819/9819 [==============================] - 109s 11ms/step - loss: 0.2369 - categorical_accuracy: 0.9774 - precision: 0.9807 - recall: 0.9771 - f1_score: 0.7991 - val_loss: 0.2362 - val_categorical_accuracy: 0.9450 - val_precision: 0.9467 - val_recall: 0.9438 - val_f1_score: 0.7547
Epoch 39/50
9819/9819 [==============================] - 105s 11ms/step - loss: 0.2367 - categorical_accuracy: 0.9780 - precision: 0.9813 - recall: 0.9776 - f1_score: 0.7973 - val_loss: 0.2373 - val_categorical_accuracy: 0.9439 - val_precision: 0.9453 - val_recall: 0.9428 - val_f1_score: 0.7562
Epoch 40/50
9819/9819 [==============================] - 106s 11ms/step - loss: 0.2344 - categorical_accuracy: 0.9783 - precision: 0.9816 - recall: 0.9779 - f1_score: 0.8005 - val_loss: 0.2433 - val_categorical_accuracy: 0.9416 - val_precision: 0.9434 - val_recall: 0.9407 - val_f1_score: 0.7543
Val Classification Report
precision recall f1-score support
0 0.97 0.92 0.94 12978
1 0.84 0.98 0.90 3780
2 1.00 0.98 0.99 360
3 0.99 0.93 0.96 4650
4 0.83 0.88 0.86 2539
5 0.98 0.98 0.98 14123
6 1.00 0.98 0.99 5400
7 0.00 0.00 0.00 14
8 0.90 0.98 0.94 748
101 0.92 0.90 0.91 7435
102 0.65 0.98 0.78 81
105 0.87 0.98 0.92 2004
106 0.96 0.99 0.98 2160
107 0.77 0.85 0.80 635
108 0.98 0.95 0.97 1772
accuracy 0.95 58679
macro avg 0.84 0.89 0.86 58679
weighted avg 0.95 0.95 0.95 58679
Test Classification Report
precision recall f1-score support
0 0.98 0.93 0.95 33789
1 0.88 0.93 0.90 9399
2 0.94 0.95 0.95 1097
3 0.99 0.98 0.98 15400
4 0.81 0.99 0.89 6246
5 0.99 0.97 0.98 35960
6 0.99 0.98 0.98 12914
7 0.00 0.00 0.00 87
8 0.81 0.99 0.89 1746
101 0.93 0.91 0.92 18727
102 0.74 0.77 0.75 452
105 0.85 0.97 0.91 5151
106 0.95 0.97 0.96 5173
107 0.95 0.99 0.97 11580
108 0.98 0.92 0.95 4974
accuracy 0.95 162695
macro avg 0.85 0.88 0.87 162695
weighted avg 0.96 0.95 0.95 162695
All Data Classification Report
precision recall f1-score support
0 0.98 0.96 0.97 164820
1 0.93 0.97 0.95 48436
2 0.97 0.98 0.98 6042
3 1.00 0.99 0.99 77554
4 0.93 0.99 0.96 31211
5 1.00 0.98 0.99 176186
6 0.99 0.98 0.99 64648
7 0.00 0.00 0.00 1815
8 0.91 0.99 0.95 10059
101 0.95 0.95 0.95 87937
102 0.78 0.92 0.84 2339
105 0.91 0.99 0.95 31150
106 0.96 0.98 0.97 25955
107 0.97 0.99 0.98 38338
108 0.99 0.96 0.97 23961
accuracy 0.97 790451
macro avg 0.89 0.91 0.90 790451
weighted avg 0.97 0.97 0.97 790451
model weights saved in rnn_model_batch_size_64-seq_length_30-2022-09-08T19_48_42.h5 Wall time: 1h 13min 11s
class CNNModel(tf.keras.Model):
def __init__(self):
super().__init__()
self.norm = layers.LayerNormalization()
self.conv1 = layers.Conv1D(filters=128, kernel_size=2, padding="same", activation='relu', name='conv1')
self.MP1 = layers.MaxPooling1D(pool_size=2, name='MP1')
self.drop1 = layers.Dropout(0.2, seed=200560, name='drop1')
self.conv2 = layers.Conv1D(filters=128, kernel_size=2, padding="same", activation='relu', name='conv2')
self.MP2 = layers.MaxPooling1D(pool_size=2, name='MP2')
self.drop2 = layers.Dropout(0.2, seed=200560, name='drop2')
self.conv3 = layers.Conv1D(filters=128, kernel_size=2, padding="same", activation='relu', name='conv3')
self.MP3 = layers.MaxPooling1D(pool_size=2, name='MP3')
self.drop3 = layers.Dropout(0.2, seed=200560, name='drop3')
self.flatten = layers.Flatten()
self.dense = layers.Dense(17, activation="relu", name='dense')
self.softmax = layers.Softmax()
return
def call(self, inputs, training=False):
x = self.norm(inputs)
x = self.conv1(x)
x = self.MP1(x)
x = self.drop1(x, training=training)
x = self.conv2(x)
x = self.MP2(x)
x = self.drop2(x, training=training)
x = self.conv3(x)
x = self.MP3(x)
x = self.drop3(x, training=training)
x = self.flatten(x)
x = self.dense(x)
return self.softmax(x)
%%time
train_df, test_df, val_df = dset.split(real=True, simul=True, drawn=True, test_size=0.2, val_size=0.1)
tf.keras.backend.clear_session()
tf.compat.v1.reset_default_graph()
cnn_model = kmodel('CNNModel', CNNModel, flist0, 'class', categories, 64, 30,
train_df, val_df, test_df, 'D:/datatmp', reset_ts=False, class_bal=True)
cnn_model.compile_and_fit(max_epochs=50, patience=10, lr=0.0001)
cnn_model.save()
Instances Train: 1425 Test: 397 Validation: 159
Model: "cnn_model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
layer_normalization (LayerN (64, 30, 16) 32
ormalization)
conv1 (Conv1D) (64, 30, 128) 4224
MP1 (MaxPooling1D) (64, 15, 128) 0
drop1 (Dropout) (64, 15, 128) 0
conv2 (Conv1D) (64, 15, 128) 32896
MP2 (MaxPooling1D) (64, 7, 128) 0
drop2 (Dropout) (64, 7, 128) 0
conv3 (Conv1D) (64, 7, 128) 32896
MP3 (MaxPooling1D) (64, 3, 128) 0
drop3 (Dropout) (64, 3, 128) 0
flatten (Flatten) (64, 384) 0
dense (Dense) (64, 17) 6545
softmax (Softmax) (64, 17) 0
=================================================================
Total params: 76,593
Trainable params: 76,593
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/50
9819/9819 [==============================] - 184s 19ms/step - loss: 1.3076 - categorical_accuracy: 0.6411 - precision: 0.8336 - recall: 0.4975 - f1_score: 0.4811 - val_loss: 0.7030 - val_categorical_accuracy: 0.7467 - val_precision: 0.8424 - val_recall: 0.6463 - val_f1_score: 0.5976
Epoch 2/50
9819/9819 [==============================] - 108s 11ms/step - loss: 0.6757 - categorical_accuracy: 0.8051 - precision: 0.8613 - recall: 0.7486 - f1_score: 0.6418 - val_loss: 0.4856 - val_categorical_accuracy: 0.8363 - val_precision: 0.8810 - val_recall: 0.7658 - val_f1_score: 0.6334
Epoch 3/50
9819/9819 [==============================] - 106s 11ms/step - loss: 0.5388 - categorical_accuracy: 0.8489 - precision: 0.8847 - recall: 0.8124 - f1_score: 0.6823 - val_loss: 0.3886 - val_categorical_accuracy: 0.8725 - val_precision: 0.9047 - val_recall: 0.8279 - val_f1_score: 0.6620
Epoch 4/50
9819/9819 [==============================] - 115s 12ms/step - loss: 0.4848 - categorical_accuracy: 0.8702 - precision: 0.8972 - recall: 0.8431 - f1_score: 0.7004 - val_loss: 0.3660 - val_categorical_accuracy: 0.8739 - val_precision: 0.8945 - val_recall: 0.8436 - val_f1_score: 0.6792
Epoch 5/50
9819/9819 [==============================] - 115s 12ms/step - loss: 0.4437 - categorical_accuracy: 0.8853 - precision: 0.9069 - recall: 0.8639 - f1_score: 0.7148 - val_loss: 0.3067 - val_categorical_accuracy: 0.8958 - val_precision: 0.9100 - val_recall: 0.8783 - val_f1_score: 0.6932
Epoch 6/50
9819/9819 [==============================] - 121s 12ms/step - loss: 0.4240 - categorical_accuracy: 0.8941 - precision: 0.9131 - recall: 0.8767 - f1_score: 0.7226 - val_loss: 0.2704 - val_categorical_accuracy: 0.9032 - val_precision: 0.9169 - val_recall: 0.8903 - val_f1_score: 0.7014
Epoch 7/50
9819/9819 [==============================] - 111s 11ms/step - loss: 0.4021 - categorical_accuracy: 0.9030 - precision: 0.9198 - recall: 0.8877 - f1_score: 0.7316 - val_loss: 0.2809 - val_categorical_accuracy: 0.9011 - val_precision: 0.9157 - val_recall: 0.8883 - val_f1_score: 0.7177
Epoch 8/50
9819/9819 [==============================] - 108s 11ms/step - loss: 0.3864 - categorical_accuracy: 0.9076 - precision: 0.9222 - recall: 0.8944 - f1_score: 0.7361 - val_loss: 0.2520 - val_categorical_accuracy: 0.9126 - val_precision: 0.9260 - val_recall: 0.8979 - val_f1_score: 0.7198
Epoch 9/50
9819/9819 [==============================] - 111s 11ms/step - loss: 0.3761 - categorical_accuracy: 0.9148 - precision: 0.9276 - recall: 0.9033 - f1_score: 0.7406 - val_loss: 0.2730 - val_categorical_accuracy: 0.9029 - val_precision: 0.9168 - val_recall: 0.8844 - val_f1_score: 0.7085
Epoch 10/50
9819/9819 [==============================] - 109s 11ms/step - loss: 0.3649 - categorical_accuracy: 0.9171 - precision: 0.9299 - recall: 0.9061 - f1_score: 0.7428 - val_loss: 0.2332 - val_categorical_accuracy: 0.9186 - val_precision: 0.9290 - val_recall: 0.9064 - val_f1_score: 0.7260
Epoch 11/50
9819/9819 [==============================] - 110s 11ms/step - loss: 0.3524 - categorical_accuracy: 0.9211 - precision: 0.9320 - recall: 0.9111 - f1_score: 0.7465 - val_loss: 0.2706 - val_categorical_accuracy: 0.9034 - val_precision: 0.9155 - val_recall: 0.8884 - val_f1_score: 0.7026
Epoch 12/50
9819/9819 [==============================] - 110s 11ms/step - loss: 0.3507 - categorical_accuracy: 0.9258 - precision: 0.9363 - recall: 0.9168 - f1_score: 0.7507 - val_loss: 0.2281 - val_categorical_accuracy: 0.9121 - val_precision: 0.9206 - val_recall: 0.8995 - val_f1_score: 0.7176
Epoch 13/50
9819/9819 [==============================] - 111s 11ms/step - loss: 0.3333 - categorical_accuracy: 0.9294 - precision: 0.9386 - recall: 0.9217 - f1_score: 0.7562 - val_loss: 0.2184 - val_categorical_accuracy: 0.9199 - val_precision: 0.9277 - val_recall: 0.9080 - val_f1_score: 0.7164
Epoch 14/50
9819/9819 [==============================] - 111s 11ms/step - loss: 0.3373 - categorical_accuracy: 0.9309 - precision: 0.9401 - recall: 0.9229 - f1_score: 0.7547 - val_loss: 0.2472 - val_categorical_accuracy: 0.9107 - val_precision: 0.9199 - val_recall: 0.8977 - val_f1_score: 0.7201
Epoch 15/50
9819/9819 [==============================] - 112s 11ms/step - loss: 0.3250 - categorical_accuracy: 0.9327 - precision: 0.9411 - recall: 0.9257 - f1_score: 0.7571 - val_loss: 0.1917 - val_categorical_accuracy: 0.9290 - val_precision: 0.9353 - val_recall: 0.9210 - val_f1_score: 0.7327
Epoch 16/50
9819/9819 [==============================] - 111s 11ms/step - loss: 0.3268 - categorical_accuracy: 0.9358 - precision: 0.9434 - recall: 0.9294 - f1_score: 0.7594 - val_loss: 0.2532 - val_categorical_accuracy: 0.9085 - val_precision: 0.9158 - val_recall: 0.8902 - val_f1_score: 0.7147
Epoch 17/50
9819/9819 [==============================] - 113s 11ms/step - loss: 0.3191 - categorical_accuracy: 0.9372 - precision: 0.9447 - recall: 0.9318 - f1_score: 0.7613 - val_loss: 0.2085 - val_categorical_accuracy: 0.9241 - val_precision: 0.9308 - val_recall: 0.9121 - val_f1_score: 0.7244
Epoch 18/50
9819/9819 [==============================] - 110s 11ms/step - loss: 0.3128 - categorical_accuracy: 0.9395 - precision: 0.9467 - recall: 0.9339 - f1_score: 0.7634 - val_loss: 0.2015 - val_categorical_accuracy: 0.9245 - val_precision: 0.9309 - val_recall: 0.9159 - val_f1_score: 0.7149
Epoch 19/50
9819/9819 [==============================] - 110s 11ms/step - loss: 0.3126 - categorical_accuracy: 0.9417 - precision: 0.9484 - recall: 0.9366 - f1_score: 0.7645 - val_loss: 0.1984 - val_categorical_accuracy: 0.9278 - val_precision: 0.9330 - val_recall: 0.9200 - val_f1_score: 0.7345
Epoch 20/50
9819/9819 [==============================] - 107s 11ms/step - loss: 0.3060 - categorical_accuracy: 0.9429 - precision: 0.9493 - recall: 0.9386 - f1_score: 0.7670 - val_loss: 0.2007 - val_categorical_accuracy: 0.9295 - val_precision: 0.9330 - val_recall: 0.9203 - val_f1_score: 0.7359
Epoch 21/50
9819/9819 [==============================] - 110s 11ms/step - loss: 0.3016 - categorical_accuracy: 0.9431 - precision: 0.9493 - recall: 0.9389 - f1_score: 0.7673 - val_loss: 0.2094 - val_categorical_accuracy: 0.9267 - val_precision: 0.9325 - val_recall: 0.9175 - val_f1_score: 0.7286
Epoch 22/50
9819/9819 [==============================] - 112s 11ms/step - loss: 0.3021 - categorical_accuracy: 0.9452 - precision: 0.9512 - recall: 0.9416 - f1_score: 0.7698 - val_loss: 0.1704 - val_categorical_accuracy: 0.9418 - val_precision: 0.9455 - val_recall: 0.9344 - val_f1_score: 0.7419
Epoch 23/50
9819/9819 [==============================] - 112s 11ms/step - loss: 0.2990 - categorical_accuracy: 0.9471 - precision: 0.9529 - recall: 0.9436 - f1_score: 0.7689 - val_loss: 0.1904 - val_categorical_accuracy: 0.9364 - val_precision: 0.9399 - val_recall: 0.9297 - val_f1_score: 0.7388
Epoch 24/50
9819/9819 [==============================] - 107s 11ms/step - loss: 0.2935 - categorical_accuracy: 0.9483 - precision: 0.9540 - recall: 0.9445 - f1_score: 0.7721 - val_loss: 0.2310 - val_categorical_accuracy: 0.9139 - val_precision: 0.9186 - val_recall: 0.9072 - val_f1_score: 0.7180
Epoch 25/50
9819/9819 [==============================] - 106s 11ms/step - loss: 0.2943 - categorical_accuracy: 0.9495 - precision: 0.9552 - recall: 0.9464 - f1_score: 0.7731 - val_loss: 0.1633 - val_categorical_accuracy: 0.9379 - val_precision: 0.9427 - val_recall: 0.9320 - val_f1_score: 0.7462
Epoch 26/50
9819/9819 [==============================] - 110s 11ms/step - loss: 0.2909 - categorical_accuracy: 0.9499 - precision: 0.9552 - recall: 0.9470 - f1_score: 0.7722 - val_loss: 0.1689 - val_categorical_accuracy: 0.9422 - val_precision: 0.9449 - val_recall: 0.9359 - val_f1_score: 0.7395
Epoch 27/50
9819/9819 [==============================] - 111s 11ms/step - loss: 0.2929 - categorical_accuracy: 0.9512 - precision: 0.9565 - recall: 0.9488 - f1_score: 0.7736 - val_loss: 0.1887 - val_categorical_accuracy: 0.9344 - val_precision: 0.9412 - val_recall: 0.9282 - val_f1_score: 0.7148
Epoch 28/50
9819/9819 [==============================] - 110s 11ms/step - loss: 0.2850 - categorical_accuracy: 0.9526 - precision: 0.9579 - recall: 0.9499 - f1_score: 0.7760 - val_loss: 0.1928 - val_categorical_accuracy: 0.9270 - val_precision: 0.9306 - val_recall: 0.9224 - val_f1_score: 0.7361
Epoch 29/50
9819/9819 [==============================] - 110s 11ms/step - loss: 0.2926 - categorical_accuracy: 0.9530 - precision: 0.9579 - recall: 0.9503 - f1_score: 0.7765 - val_loss: 0.1627 - val_categorical_accuracy: 0.9437 - val_precision: 0.9467 - val_recall: 0.9397 - val_f1_score: 0.7409
Epoch 30/50
9819/9819 [==============================] - 112s 11ms/step - loss: 0.2836 - categorical_accuracy: 0.9546 - precision: 0.9594 - recall: 0.9522 - f1_score: 0.7766 - val_loss: 0.1625 - val_categorical_accuracy: 0.9424 - val_precision: 0.9464 - val_recall: 0.9386 - val_f1_score: 0.7432
Epoch 31/50
9819/9819 [==============================] - 110s 11ms/step - loss: 0.2820 - categorical_accuracy: 0.9544 - precision: 0.9593 - recall: 0.9523 - f1_score: 0.7756 - val_loss: 0.1828 - val_categorical_accuracy: 0.9362 - val_precision: 0.9406 - val_recall: 0.9316 - val_f1_score: 0.7251
Epoch 32/50
9819/9819 [==============================] - 108s 11ms/step - loss: 0.2778 - categorical_accuracy: 0.9556 - precision: 0.9603 - recall: 0.9537 - f1_score: 0.7781 - val_loss: 0.1589 - val_categorical_accuracy: 0.9446 - val_precision: 0.9476 - val_recall: 0.9411 - val_f1_score: 0.7485
Epoch 33/50
9819/9819 [==============================] - 107s 11ms/step - loss: 0.2782 - categorical_accuracy: 0.9558 - precision: 0.9607 - recall: 0.9539 - f1_score: 0.7775 - val_loss: 0.1799 - val_categorical_accuracy: 0.9424 - val_precision: 0.9459 - val_recall: 0.9389 - val_f1_score: 0.7451
Epoch 34/50
9819/9819 [==============================] - 107s 11ms/step - loss: 0.2740 - categorical_accuracy: 0.9566 - precision: 0.9613 - recall: 0.9548 - f1_score: 0.7788 - val_loss: 0.1643 - val_categorical_accuracy: 0.9399 - val_precision: 0.9448 - val_recall: 0.9350 - val_f1_score: 0.7380
Epoch 35/50
9819/9819 [==============================] - 108s 11ms/step - loss: 0.2722 - categorical_accuracy: 0.9579 - precision: 0.9627 - recall: 0.9561 - f1_score: 0.7809 - val_loss: 0.1827 - val_categorical_accuracy: 0.9363 - val_precision: 0.9430 - val_recall: 0.9293 - val_f1_score: 0.7453
Epoch 36/50
9819/9819 [==============================] - 108s 11ms/step - loss: 0.2751 - categorical_accuracy: 0.9577 - precision: 0.9625 - recall: 0.9560 - f1_score: 0.7796 - val_loss: 0.1629 - val_categorical_accuracy: 0.9440 - val_precision: 0.9472 - val_recall: 0.9410 - val_f1_score: 0.7462
Epoch 37/50
9819/9819 [==============================] - 108s 11ms/step - loss: 0.2712 - categorical_accuracy: 0.9585 - precision: 0.9630 - recall: 0.9570 - f1_score: 0.7801 - val_loss: 0.1624 - val_categorical_accuracy: 0.9422 - val_precision: 0.9468 - val_recall: 0.9381 - val_f1_score: 0.7306
Epoch 38/50
9819/9819 [==============================] - 109s 11ms/step - loss: 0.2723 - categorical_accuracy: 0.9595 - precision: 0.9639 - recall: 0.9577 - f1_score: 0.7811 - val_loss: 0.1837 - val_categorical_accuracy: 0.9378 - val_precision: 0.9405 - val_recall: 0.9348 - val_f1_score: 0.7446
Epoch 39/50
9819/9819 [==============================] - 108s 11ms/step - loss: 0.2708 - categorical_accuracy: 0.9602 - precision: 0.9646 - recall: 0.9586 - f1_score: 0.7820 - val_loss: 0.1598 - val_categorical_accuracy: 0.9396 - val_precision: 0.9446 - val_recall: 0.9365 - val_f1_score: 0.7414
Epoch 40/50
9819/9819 [==============================] - 112s 11ms/step - loss: 0.2672 - categorical_accuracy: 0.9596 - precision: 0.9643 - recall: 0.9582 - f1_score: 0.7829 - val_loss: 0.1601 - val_categorical_accuracy: 0.9430 - val_precision: 0.9478 - val_recall: 0.9382 - val_f1_score: 0.7433
Epoch 41/50
9819/9819 [==============================] - 112s 11ms/step - loss: 0.2652 - categorical_accuracy: 0.9612 - precision: 0.9658 - recall: 0.9598 - f1_score: 0.7842 - val_loss: 0.1664 - val_categorical_accuracy: 0.9492 - val_precision: 0.9535 - val_recall: 0.9452 - val_f1_score: 0.7466
Epoch 42/50
9819/9819 [==============================] - 113s 11ms/step - loss: 0.2661 - categorical_accuracy: 0.9610 - precision: 0.9656 - recall: 0.9597 - f1_score: 0.7823 - val_loss: 0.1480 - val_categorical_accuracy: 0.9454 - val_precision: 0.9482 - val_recall: 0.9434 - val_f1_score: 0.7514
Epoch 43/50
9819/9819 [==============================] - 108s 11ms/step - loss: 0.2682 - categorical_accuracy: 0.9612 - precision: 0.9656 - recall: 0.9601 - f1_score: 0.7828 - val_loss: 0.1591 - val_categorical_accuracy: 0.9474 - val_precision: 0.9523 - val_recall: 0.9409 - val_f1_score: 0.7458
Epoch 44/50
9819/9819 [==============================] - 109s 11ms/step - loss: 0.2617 - categorical_accuracy: 0.9625 - precision: 0.9668 - recall: 0.9613 - f1_score: 0.7847 - val_loss: 0.1602 - val_categorical_accuracy: 0.9473 - val_precision: 0.9515 - val_recall: 0.9377 - val_f1_score: 0.7435
Epoch 45/50
9819/9819 [==============================] - 109s 11ms/step - loss: 0.2603 - categorical_accuracy: 0.9629 - precision: 0.9673 - recall: 0.9618 - f1_score: 0.7860 - val_loss: 0.1615 - val_categorical_accuracy: 0.9439 - val_precision: 0.9472 - val_recall: 0.9406 - val_f1_score: 0.7461
Epoch 46/50
9819/9819 [==============================] - 109s 11ms/step - loss: 0.2647 - categorical_accuracy: 0.9623 - precision: 0.9666 - recall: 0.9612 - f1_score: 0.7838 - val_loss: 0.1443 - val_categorical_accuracy: 0.9472 - val_precision: 0.9508 - val_recall: 0.9440 - val_f1_score: 0.7413
Epoch 47/50
9819/9819 [==============================] - 107s 11ms/step - loss: 0.2617 - categorical_accuracy: 0.9638 - precision: 0.9681 - recall: 0.9624 - f1_score: 0.7855 - val_loss: 0.1685 - val_categorical_accuracy: 0.9421 - val_precision: 0.9466 - val_recall: 0.9383 - val_f1_score: 0.7410
Epoch 48/50
9819/9819 [==============================] - 108s 11ms/step - loss: 0.2644 - categorical_accuracy: 0.9637 - precision: 0.9680 - recall: 0.9625 - f1_score: 0.7846 - val_loss: 0.1739 - val_categorical_accuracy: 0.9424 - val_precision: 0.9459 - val_recall: 0.9390 - val_f1_score: 0.7418
Epoch 49/50
9819/9819 [==============================] - 110s 11ms/step - loss: 0.2598 - categorical_accuracy: 0.9640 - precision: 0.9682 - recall: 0.9628 - f1_score: 0.7866 - val_loss: 0.1655 - val_categorical_accuracy: 0.9473 - val_precision: 0.9504 - val_recall: 0.9450 - val_f1_score: 0.7470
Epoch 50/50
9819/9819 [==============================] - 111s 11ms/step - loss: 0.2574 - categorical_accuracy: 0.9652 - precision: 0.9693 - recall: 0.9640 - f1_score: 0.7878 - val_loss: 0.1595 - val_categorical_accuracy: 0.9404 - val_precision: 0.9445 - val_recall: 0.9380 - val_f1_score: 0.7426
Val Classification Report
precision recall f1-score support
0 0.97 0.90 0.93 12978
1 0.84 0.97 0.90 3780
2 1.00 0.97 0.98 360
3 0.99 0.96 0.97 4650
4 0.90 0.88 0.89 2539
5 1.00 0.98 0.99 14123
6 1.00 0.98 0.99 5400
7 0.00 0.00 0.00 14
8 0.95 0.97 0.96 748
101 0.89 0.90 0.90 7435
102 0.52 0.83 0.64 81
105 0.86 0.98 0.92 2004
106 0.94 1.00 0.97 2160
107 0.67 0.89 0.76 635
108 0.98 0.98 0.98 1772
accuracy 0.95 58679
macro avg 0.83 0.88 0.85 58679
weighted avg 0.95 0.95 0.95 58679
Test Classification Report
precision recall f1-score support
0 0.98 0.88 0.93 33789
1 0.90 0.89 0.90 9399
2 0.93 0.99 0.96 1097
3 0.99 0.98 0.99 15400
4 0.85 0.98 0.91 6246
5 1.00 0.98 0.99 35960
6 0.99 0.97 0.98 12914
7 0.00 0.00 0.00 87
8 0.85 0.98 0.91 1746
101 0.87 0.93 0.90 18727
102 0.66 0.63 0.65 452
105 0.82 0.97 0.89 5151
106 0.92 0.98 0.95 5173
107 0.89 1.00 0.94 11580
108 0.98 0.94 0.96 4974
accuracy 0.95 162695
macro avg 0.84 0.87 0.86 162695
weighted avg 0.95 0.95 0.95 162695
All Data Classification Report
precision recall f1-score support
0 0.98 0.92 0.95 164820
1 0.94 0.94 0.94 48436
2 0.98 0.99 0.98 6042
3 1.00 0.99 0.99 77554
4 0.93 0.98 0.96 31211
5 1.00 0.98 0.99 176186
6 1.00 0.97 0.98 64648
7 0.00 0.00 0.00 1815
8 0.94 0.98 0.96 10059
101 0.90 0.95 0.93 87937
102 0.69 0.90 0.78 2339
105 0.89 0.99 0.94 31150
106 0.93 0.99 0.96 25955
107 0.91 1.00 0.95 38338
108 0.99 0.97 0.98 23961
accuracy 0.96 790451
macro avg 0.87 0.90 0.89 790451
weighted avg 0.96 0.96 0.96 790451
model weights saved in cnn_model_batch_size_64-seq_length_30-2022-09-08T06_49_06.h5 Wall time: 1h 35min 59s
class MixedModel(tf.keras.Model):
def __init__(self):
super().__init__()
# CNN
self.norm = layers.LayerNormalization(name='CNN_norm')
self.conv1 = layers.Conv1D(filters=128, kernel_size=2, padding="same", activation='relu', name='conv1')
self.MP1 = layers.MaxPooling1D(pool_size=2, name='MP1')
self.drop1 = layers.Dropout(0.2, seed=200560, name='drop1')
self.conv2 = layers.Conv1D(filters=128, kernel_size=2, padding="same", activation='relu', name='conv2')
self.MP2 = layers.MaxPooling1D(pool_size=2, name='MP2')
self.drop2 = layers.Dropout(0.2, seed=200560, name='drop2')
self.conv3 = layers.Conv1D(filters=128, kernel_size=2, padding="same", activation='relu', name='conv3')
self.MP3 = layers.MaxPooling1D(pool_size=2, name='MP3')
self.drop3 = layers.Dropout(0.2, seed=200560, name='drop3')
self.flatten = layers.Flatten(name='CNN_flatten')
#RNN
self.lstm = layers.LSTM(128, return_sequences=False, dropout=0.15, name='lstm')
#Dense
self.dense = layers.Dense(17, activation="relu", name='dense')
self.softmax = layers.Softmax(name='softmax')
def call(self, inputs, training=False):
x = self.norm(inputs)
x = self.conv1(x)
x = self.MP1(x)
x = self.drop1(x, training=training)
x = self.conv2(x)
x = self.MP2(x)
x = self.drop2(x, training=training)
x = self.conv3(x)
x = self.MP3(x)
x = self.drop3(x, training=training)
x = self.flatten(x)
y = self.lstm(inputs, training=training)
z = tf.concat([x, y], 1)
z = self.dense(z)
return self.softmax(z)
%%time
train_df, test_df, val_df = dset.split(real=True, simul=True, drawn=True, test_size=0.2, val_size=0.1)
tf.keras.backend.clear_session()
tf.compat.v1.reset_default_graph()
mixed_model = kmodel('MixedModel', MixedModel, flist0, 'class', categories, 64, 30,
train_df, val_df, test_df, 'D:/datatmp', reset_ts=True, class_bal=True)
mixed_model.compile_and_fit(max_epochs=100, patience=10, lr=0.0001)
Instances Train: 1425 Test: 397 Validation: 159
Model: "mixed_model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
CNN_norm (LayerNormalizatio (64, 30, 16) 32
n)
conv1 (Conv1D) (64, 30, 128) 4224
MP1 (MaxPooling1D) (64, 15, 128) 0
drop1 (Dropout) (64, 15, 128) 0
conv2 (Conv1D) (64, 15, 128) 32896
MP2 (MaxPooling1D) (64, 7, 128) 0
drop2 (Dropout) (64, 7, 128) 0
conv3 (Conv1D) (64, 7, 128) 32896
MP3 (MaxPooling1D) (64, 3, 128) 0
drop3 (Dropout) (64, 3, 128) 0
CNN_flatten (Flatten) (64, 384) 0
lstm (LSTM) (64, 128) 74240
dense (Dense) (64, 17) 8721
softmax (Softmax) (64, 17) 0
=================================================================
Total params: 153,009
Trainable params: 153,009
Non-trainable params: 0
_________________________________________________________________
None
Epoch 1/100
9819/9819 [==============================] - 330s 33ms/step - loss: 1.1338 - categorical_accuracy: 0.6731 - precision: 0.8550 - recall: 0.5317 - f1_score: 0.5241 - val_loss: 0.6545 - val_categorical_accuracy: 0.7712 - val_precision: 0.8519 - val_recall: 0.6655 - val_f1_score: 0.6060
Epoch 2/100
9819/9819 [==============================] - 148s 15ms/step - loss: 0.6045 - categorical_accuracy: 0.8280 - precision: 0.8779 - recall: 0.7757 - f1_score: 0.6662 - val_loss: 0.4053 - val_categorical_accuracy: 0.8731 - val_precision: 0.9049 - val_recall: 0.8276 - val_f1_score: 0.6622
Epoch 3/100
9819/9819 [==============================] - 150s 15ms/step - loss: 0.4973 - categorical_accuracy: 0.8690 - precision: 0.8998 - recall: 0.8394 - f1_score: 0.7031 - val_loss: 0.4333 - val_categorical_accuracy: 0.8570 - val_precision: 0.8961 - val_recall: 0.8002 - val_f1_score: 0.6580
Epoch 4/100
9819/9819 [==============================] - 150s 15ms/step - loss: 0.4357 - categorical_accuracy: 0.8899 - precision: 0.9122 - recall: 0.8675 - f1_score: 0.7204 - val_loss: 0.3056 - val_categorical_accuracy: 0.8898 - val_precision: 0.9080 - val_recall: 0.8652 - val_f1_score: 0.7064
Epoch 5/100
9819/9819 [==============================] - 148s 15ms/step - loss: 0.4137 - categorical_accuracy: 0.9050 - precision: 0.9223 - recall: 0.8873 - f1_score: 0.7305 - val_loss: 0.2479 - val_categorical_accuracy: 0.9188 - val_precision: 0.9294 - val_recall: 0.9087 - val_f1_score: 0.7121
Epoch 6/100
9819/9819 [==============================] - 151s 15ms/step - loss: 0.3772 - categorical_accuracy: 0.9157 - precision: 0.9297 - recall: 0.9020 - f1_score: 0.7424 - val_loss: 0.3250 - val_categorical_accuracy: 0.8777 - val_precision: 0.8923 - val_recall: 0.8579 - val_f1_score: 0.6920
Epoch 7/100
9819/9819 [==============================] - 154s 16ms/step - loss: 0.3638 - categorical_accuracy: 0.9214 - precision: 0.9332 - recall: 0.9108 - f1_score: 0.7456 - val_loss: 0.2710 - val_categorical_accuracy: 0.9088 - val_precision: 0.9197 - val_recall: 0.8888 - val_f1_score: 0.6993
Epoch 8/100
9819/9819 [==============================] - 148s 15ms/step - loss: 0.3461 - categorical_accuracy: 0.9270 - precision: 0.9377 - recall: 0.9176 - f1_score: 0.7534 - val_loss: 0.2629 - val_categorical_accuracy: 0.9123 - val_precision: 0.9227 - val_recall: 0.8975 - val_f1_score: 0.7001
Epoch 9/100
9819/9819 [==============================] - 146s 15ms/step - loss: 0.3487 - categorical_accuracy: 0.9307 - precision: 0.9402 - recall: 0.9220 - f1_score: 0.7537 - val_loss: 0.2489 - val_categorical_accuracy: 0.9097 - val_precision: 0.9206 - val_recall: 0.8995 - val_f1_score: 0.7028
Epoch 10/100
9819/9819 [==============================] - 152s 15ms/step - loss: 0.3250 - categorical_accuracy: 0.9351 - precision: 0.9434 - recall: 0.9281 - f1_score: 0.7605 - val_loss: 0.2429 - val_categorical_accuracy: 0.9164 - val_precision: 0.9249 - val_recall: 0.9053 - val_f1_score: 0.6989
Epoch 11/100
9819/9819 [==============================] - 155s 16ms/step - loss: 0.3206 - categorical_accuracy: 0.9394 - precision: 0.9473 - recall: 0.9328 - f1_score: 0.7632 - val_loss: 0.2105 - val_categorical_accuracy: 0.9247 - val_precision: 0.9326 - val_recall: 0.9192 - val_f1_score: 0.7342
Epoch 12/100
9819/9819 [==============================] - 153s 16ms/step - loss: 0.3125 - categorical_accuracy: 0.9421 - precision: 0.9491 - recall: 0.9366 - f1_score: 0.7653 - val_loss: 0.2176 - val_categorical_accuracy: 0.9235 - val_precision: 0.9290 - val_recall: 0.9148 - val_f1_score: 0.7221
Epoch 13/100
9819/9819 [==============================] - 153s 16ms/step - loss: 0.3111 - categorical_accuracy: 0.9455 - precision: 0.9521 - recall: 0.9401 - f1_score: 0.7670 - val_loss: 0.1829 - val_categorical_accuracy: 0.9329 - val_precision: 0.9393 - val_recall: 0.9280 - val_f1_score: 0.7286
Epoch 14/100
9819/9819 [==============================] - 147s 15ms/step - loss: 0.2964 - categorical_accuracy: 0.9484 - precision: 0.9544 - recall: 0.9443 - f1_score: 0.7729 - val_loss: 0.1681 - val_categorical_accuracy: 0.9419 - val_precision: 0.9462 - val_recall: 0.9388 - val_f1_score: 0.7332
Epoch 15/100
9819/9819 [==============================] - 146s 15ms/step - loss: 0.2933 - categorical_accuracy: 0.9499 - precision: 0.9557 - recall: 0.9462 - f1_score: 0.7737 - val_loss: 0.2068 - val_categorical_accuracy: 0.9276 - val_precision: 0.9328 - val_recall: 0.9212 - val_f1_score: 0.7374
Epoch 16/100
9819/9819 [==============================] - 150s 15ms/step - loss: 0.2948 - categorical_accuracy: 0.9523 - precision: 0.9580 - recall: 0.9485 - f1_score: 0.7738 - val_loss: 0.1975 - val_categorical_accuracy: 0.9318 - val_precision: 0.9368 - val_recall: 0.9259 - val_f1_score: 0.7257
Epoch 17/100
9819/9819 [==============================] - 151s 15ms/step - loss: 0.2893 - categorical_accuracy: 0.9543 - precision: 0.9595 - recall: 0.9511 - f1_score: 0.7764 - val_loss: 0.1754 - val_categorical_accuracy: 0.9410 - val_precision: 0.9464 - val_recall: 0.9363 - val_f1_score: 0.7517
Epoch 18/100
9819/9819 [==============================] - 150s 15ms/step - loss: 0.2827 - categorical_accuracy: 0.9558 - precision: 0.9607 - recall: 0.9528 - f1_score: 0.7777 - val_loss: 0.2170 - val_categorical_accuracy: 0.9245 - val_precision: 0.9301 - val_recall: 0.9198 - val_f1_score: 0.7105
Epoch 19/100
9819/9819 [==============================] - 151s 15ms/step - loss: 0.2763 - categorical_accuracy: 0.9565 - precision: 0.9615 - recall: 0.9540 - f1_score: 0.7798 - val_loss: 0.1697 - val_categorical_accuracy: 0.9406 - val_precision: 0.9441 - val_recall: 0.9373 - val_f1_score: 0.7375
Epoch 20/100
9819/9819 [==============================] - 152s 15ms/step - loss: 0.2749 - categorical_accuracy: 0.9586 - precision: 0.9633 - recall: 0.9566 - f1_score: 0.7817 - val_loss: 0.1589 - val_categorical_accuracy: 0.9437 - val_precision: 0.9468 - val_recall: 0.9412 - val_f1_score: 0.7384
Epoch 21/100
9819/9819 [==============================] - 160s 16ms/step - loss: 0.2747 - categorical_accuracy: 0.9597 - precision: 0.9644 - recall: 0.9577 - f1_score: 0.7815 - val_loss: 0.1510 - val_categorical_accuracy: 0.9483 - val_precision: 0.9520 - val_recall: 0.9445 - val_f1_score: 0.7385
Epoch 22/100
9819/9819 [==============================] - 150s 15ms/step - loss: 0.2730 - categorical_accuracy: 0.9603 - precision: 0.9649 - recall: 0.9581 - f1_score: 0.7817 - val_loss: 0.1769 - val_categorical_accuracy: 0.9418 - val_precision: 0.9461 - val_recall: 0.9361 - val_f1_score: 0.7378
Epoch 23/100
9819/9819 [==============================] - 151s 15ms/step - loss: 0.2701 - categorical_accuracy: 0.9624 - precision: 0.9666 - recall: 0.9605 - f1_score: 0.7836 - val_loss: 0.1881 - val_categorical_accuracy: 0.9336 - val_precision: 0.9377 - val_recall: 0.9308 - val_f1_score: 0.7313
Epoch 24/100
9819/9819 [==============================] - 148s 15ms/step - loss: 0.2684 - categorical_accuracy: 0.9622 - precision: 0.9665 - recall: 0.9605 - f1_score: 0.7843 - val_loss: 0.1649 - val_categorical_accuracy: 0.9433 - val_precision: 0.9461 - val_recall: 0.9403 - val_f1_score: 0.7476
Epoch 25/100
9819/9819 [==============================] - 147s 15ms/step - loss: 0.2679 - categorical_accuracy: 0.9637 - precision: 0.9680 - recall: 0.9620 - f1_score: 0.7858 - val_loss: 0.1847 - val_categorical_accuracy: 0.9391 - val_precision: 0.9426 - val_recall: 0.9358 - val_f1_score: 0.7393
Epoch 26/100
9819/9819 [==============================] - 148s 15ms/step - loss: 0.2692 - categorical_accuracy: 0.9648 - precision: 0.9689 - recall: 0.9632 - f1_score: 0.7843 - val_loss: 0.1695 - val_categorical_accuracy: 0.9445 - val_precision: 0.9473 - val_recall: 0.9419 - val_f1_score: 0.7435
Epoch 27/100
9819/9819 [==============================] - 150s 15ms/step - loss: 0.2582 - categorical_accuracy: 0.9653 - precision: 0.9694 - recall: 0.9640 - f1_score: 0.7873 - val_loss: 0.1615 - val_categorical_accuracy: 0.9443 - val_precision: 0.9477 - val_recall: 0.9416 - val_f1_score: 0.7399
Val Classification Report
precision recall f1-score support
0 0.96 0.94 0.95 12978
1 0.81 0.98 0.89 3780
2 1.00 0.97 0.98 360
3 0.94 0.97 0.95 4650
4 0.86 0.90 0.88 2539
5 0.99 0.95 0.97 14123
6 0.99 0.96 0.98 5400
7 0.00 0.00 0.00 14
8 0.92 0.98 0.95 748
101 0.98 0.85 0.91 7435
102 0.59 0.99 0.74 81
105 0.81 0.99 0.89 2004
106 0.90 1.00 0.95 2160
107 0.64 0.82 0.72 635
108 0.99 0.96 0.98 1772
accuracy 0.94 58679
macro avg 0.82 0.88 0.85 58679
weighted avg 0.95 0.94 0.94 58679
Test Classification Report
precision recall f1-score support
0 0.96 0.92 0.94 33789
1 0.87 0.96 0.92 9399
2 0.95 1.00 0.97 1097
3 0.98 0.99 0.98 15400
4 0.83 0.97 0.89 6246
5 1.00 0.96 0.98 35960
6 0.99 0.96 0.97 12914
7 0.00 0.00 0.00 87
8 0.85 0.98 0.91 1746
101 0.97 0.87 0.91 18727
102 0.75 0.75 0.75 452
105 0.71 0.98 0.83 5151
106 0.90 0.98 0.94 5173
107 0.92 1.00 0.96 11580
108 0.98 0.94 0.96 4974
accuracy 0.95 162695
macro avg 0.84 0.88 0.86 162695
weighted avg 0.95 0.95 0.95 162695
All Data Classification Report
precision recall f1-score support
0 0.96 0.94 0.95 164820
1 0.89 0.97 0.93 48436
2 0.97 0.98 0.98 6042
3 0.97 0.99 0.98 77554
4 0.89 0.98 0.93 31211
5 1.00 0.96 0.98 176186
6 1.00 0.96 0.98 64648
7 0.00 0.00 0.00 1815
8 0.93 0.97 0.95 10059
101 0.97 0.89 0.93 87937
102 0.73 0.92 0.81 2339
105 0.82 1.00 0.90 31150
106 0.90 0.99 0.95 25955
107 0.92 0.99 0.95 38338
108 0.98 0.97 0.98 23961
accuracy 0.95 790451
macro avg 0.86 0.90 0.88 790451
weighted avg 0.96 0.95 0.95 790451
Wall time: 1h 15min
dset_g = CustomDataGen(dset.drawn, flist0, 'class', categories, 64, 30, 'D:\datatmp')
fmodel = kmodel_from_file(RNNModel, (30, 16), 'RNNModel_batch_size_64-seq_length_30-2022-09-03T15_47_05.h5')
#dset_g.plot(0)
rnn_model.class_rep(dset_g)